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<div class="usecase-title">Evaluate Business Location Using Pedestrian Traffic: Day and Night</div>
<div class="usecase-authors"><b>Authored by: </b> Barkha Javed, Weiren Kong</div>

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<div class="usecase-duration"><b>Duration:</b> 75 mins</div>
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    <div class="usecase-level"><b>Level: </b>Intermediate</div>
    <div class="usecase-skill"><b>Pre-requisite Skills: </b>Python</div>
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<div class="usecase-section-header">Scenario</div>

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<p><strong>As a business owner, I want to know how much pedestrian foot traffic occurs around me during the day and night, so that I can evaluate the suitability of the location and hours for my business.</strong></p>
<p>Busy foot traffic in a business area may not always mean busy foot traffic at night.</p>
<p><strong>As a business owner, I want to know how much pedestrian foot traffic I get compared to surrounding areas, so that I can assess if it is better to adapt my hours, extend or move locations.</strong></p>
<ul>
<li><p>Foot traffic flow during day or night may indicate adapting staff levels during specific hours.</p>
</li>
<li><p>Significantly low foot traffic in comparison to other streets may mean adapting business strategy or moving location.</p>
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<li><p>Duration of steady foot traffic from early morning to mid afternoon only, may indicate adapting business hours to match.</p>
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<div class="usecase-section-header">What this use case will teach you</div>

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<p>At the end of this use case you will understand how to:</p>
<ul>
<li>Load and examine the pedestrian sensor location dataset</li>
<li>Load and examine the pedestrian counting system - monthly (counts per hour)monthly hourly counts dataset</li>
<li>Use these datasets to explore day and night foot traffic in an area</li>
<li>Use visualisations to share results</li>
<li>Use machine learning to identify if there are any common patterns in day and night traffic</li>
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<div class="usecase-section-header">Scenario</div>A brief introduction to the datasets used</div>

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<p>The datasets we examine for day and night traffic include the datsets below</p>
<p><strong><a href="https://data.melbourne.vic.gov.au/Transport/Pedestrian-Counting-System-Monthly-counts-per-hour/b2ak-trbp">Pedestrian Counting System - Monthly (counts per hour)</a></strong></p>
<p><strong><a href="https://data.melbourne.vic.gov.au/Transport/Pedestrian-Counting-System-Sensor-Locations/h57g-5234">Pedestrian Counting System - Sensor Locations</a></strong></p>
<p>The monthly hourly counts dataset contains traffic collected from sensors since 2009, it is updated monthly. It contains 10 fields including sensor id, date and time, year, month, day of year, day of week, time of day, sensor name and hourly counts.</p>
<p>The sensor locations dataset contains the details about the sensors detecting the pedestrian traffic. It contains 11 fields, the main ones of interest are sensor description, latitude, longitude, location and direction of reading.</p>

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<div class="usecase-section-header">Scenario</div>Accessing and Loading data</div>

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    <span class="icon"><i class="fab fa-python"></i></span>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#Libraries to be installed</span>
<span class="c1">##!pip -q is to give less output</span>
<span class="o">!</span>pip -q install sodapy
<span class="o">!</span>pip -q install seaborn
<span class="o">!</span>pip -q install pandas
<span class="o">!</span>pip -q install matplotlib
<span class="o">!</span>pip -q install numpy
<span class="o">!</span>pip -q install nbconvert
<span class="o">!</span>pip -q install keyboard
<span class="o">!</span>pip -q install geopandas
<span class="o">!</span>pip -q install sklearn
<span class="o">!</span>pip -q install folium
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    <span class="icon"><i class="fab fa-python"></i></span>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#load libraries</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">keyboard</span>

<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">sodapy</span> <span class="kn">import</span> <span class="n">Socrata</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>

<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>

<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="kn">import</span> <span class="nn">matplotlib.cm</span> <span class="k">as</span> <span class="nn">cm</span>
<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">colors</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">style</span>
<span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>

<span class="kn">import</span> <span class="nn">plotly.graph_objs</span> <span class="k">as</span> <span class="nn">go</span>
<span class="kn">import</span> <span class="nn">plotly.express</span> <span class="k">as</span> <span class="nn">px</span>

<span class="kn">import</span> <span class="nn">warnings</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span>

<span class="kn">import</span> <span class="nn">folium</span>
<span class="kn">from</span> <span class="nn">folium.plugins</span> <span class="kn">import</span> <span class="n">MarkerCluster</span>

<span class="c1">#Socrata client connection</span>
<span class="n">client</span> <span class="o">=</span> <span class="n">Socrata</span><span class="p">(</span><span class="s1">&#39;data.melbourne.vic.gov.au&#39;</span><span class="p">,</span> <span class="s1">&#39;9UAGAvkem9fqXDhBLElapjazL&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
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<h4 id="Load-Pedestrian-sensor-location-data">Load Pedestrian sensor location data<a class="anchor-link" href="#Load-Pedestrian-sensor-location-data">&#182;</a></h4>
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    <span class="icon"><i class="fab fa-python"></i></span>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#Pedestrian sensor location data</span>
<span class="n">sensor_data_id</span> <span class="o">=</span> <span class="s2">&quot;h57g-5234&quot;</span>
<span class="n">results</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">sensor_data_id</span><span class="p">)</span>
<span class="n">sensor_location</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_records</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
<span class="c1">#sensor_location.head(5)</span>

<span class="n">sensor_location</span><span class="p">[[</span><span class="s1">&#39;latitude&#39;</span><span class="p">,</span> <span class="s1">&#39;longitude&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">sensor_location</span><span class="p">[[</span><span class="s1">&#39;latitude&#39;</span><span class="p">,</span> <span class="s1">&#39;longitude&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
<span class="n">sensor_location</span> <span class="o">=</span> <span class="n">sensor_location</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;location&#39;</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">sensor_location</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sensor_location</span><span class="p">[</span><span class="s1">&#39;latitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">sensor_location</span><span class="p">[</span><span class="s1">&#39;lon&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sensor_location</span><span class="p">[</span><span class="s1">&#39;longitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>

<span class="c1">#View sensor data</span>
<span class="n">sensor_location</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
<span class="n">sensor_location</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
     </code>
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<pre><code class="language-python" data-lang="python">&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
RangeIndex: 91 entries, 0 to 90
Data columns (total 12 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   sensor_id           91 non-null     object 
 1   sensor_description  91 non-null     object 
 2   sensor_name         91 non-null     object 
 3   installation_date   91 non-null     object 
 4   status              91 non-null     object 
 5   note                10 non-null     object 
 6   latitude            91 non-null     float64
 7   longitude           91 non-null     float64
 8   direction_1         78 non-null     object 
 9   direction_2         78 non-null     object 
 10  lat                 91 non-null     float64
 11  lon                 91 non-null     float64
dtypes: float64(4), object(8)
memory usage: 8.7+ KB
</code>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>sensor_id</th>
      <th>sensor_description</th>
      <th>sensor_name</th>
      <th>installation_date</th>
      <th>status</th>
      <th>note</th>
      <th>latitude</th>
      <th>longitude</th>
      <th>direction_1</th>
      <th>direction_2</th>
      <th>lat</th>
      <th>lon</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>16</td>
      <td>Australia on Collins</td>
      <td>Col270_T</td>
      <td>2009-03-30T00:00:00.000</td>
      <td>R</td>
      <td>Device moved to location ID 53 (22/09/2015)</td>
      <td>-37.815734</td>
      <td>144.965210</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>-37.815734</td>
      <td>144.965210</td>
    </tr>
    <tr>
      <th>1</th>
      <td>50</td>
      <td>Faraday St-Lygon St (West)</td>
      <td>Lyg309_T</td>
      <td>2017-11-30T00:00:00.000</td>
      <td>A</td>
      <td>NaN</td>
      <td>-37.798082</td>
      <td>144.967210</td>
      <td>South</td>
      <td>North</td>
      <td>-37.798082</td>
      <td>144.967210</td>
    </tr>
    <tr>
      <th>2</th>
      <td>73</td>
      <td>Bourke St - Spencer St (South)</td>
      <td>Bou655_T</td>
      <td>2020-10-02T00:00:00.000</td>
      <td>I</td>
      <td>NaN</td>
      <td>-37.816957</td>
      <td>144.954154</td>
      <td>East</td>
      <td>West</td>
      <td>-37.816957</td>
      <td>144.954154</td>
    </tr>
    <tr>
      <th>3</th>
      <td>66</td>
      <td>State Library - New</td>
      <td>QVN_T</td>
      <td>2020-04-06T00:00:00.000</td>
      <td>A</td>
      <td>NaN</td>
      <td>-37.810578</td>
      <td>144.964443</td>
      <td>South</td>
      <td>North</td>
      <td>-37.810578</td>
      <td>144.964443</td>
    </tr>
    <tr>
      <th>4</th>
      <td>59</td>
      <td>Building 80 RMIT</td>
      <td>RMIT_T</td>
      <td>2019-02-13T00:00:00.000</td>
      <td>A</td>
      <td>NaN</td>
      <td>-37.808256</td>
      <td>144.963049</td>
      <td>South</td>
      <td>North</td>
      <td>-37.808256</td>
      <td>144.963049</td>
    </tr>
  </tbody>
</table>
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<h4 id="Load-Pedestrian-monthly-hourly-count-data">Load Pedestrian monthly hourly count data<a class="anchor-link" href="#Load-Pedestrian-monthly-hourly-count-data">&#182;</a></h4>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#Pedestrian foot count data </span>
<span class="c1">#Uncomment the below to open data source, download sensor data, and store it as a csv locally.</span>
<span class="c1"># sensor_data_id = &quot;b2ak-trbp&quot;</span>
<span class="c1"># results = client.get(sensor_data_id, limit=7000000)</span>
<span class="c1"># ds_traffic = pd.DataFrame.from_records(results)</span>
<span class="c1"># ds_traffic.to_csv(&#39;sensor_traffic.csv&#39;, index=False)</span>

<span class="n">sensor_traffic</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;sensor_traffic.csv&#39;</span><span class="p">)</span>
<span class="n">sensor_traffic</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">The shape of dataset is </span><span class="si">{</span><span class="n">sensor_traffic</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">. </span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Below are the first few rows of this dataset: &#39;</span><span class="p">)</span>
<span class="n">sensor_traffic</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
</code>
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<pre><code class="language-python" data-lang="python">&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
RangeIndex: 4562230 entries, 0 to 4562229
Data columns (total 10 columns):
 #   Column         Dtype 
---  ------         ----- 
 0   id             int64 
 1   date_time      object
 2   year           int64 
 3   month          object
 4   mdate          int64 
 5   day            object
 6   time           int64 
 7   sensor_id      int64 
 8   sensor_name    object
 9   hourly_counts  int64 
dtypes: int64(6), object(4)
memory usage: 348.1+ MB

The shape of dataset is (4562230, 10). 

Below are the first few rows of this dataset: 
</code>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>date_time</th>
      <th>year</th>
      <th>month</th>
      <th>mdate</th>
      <th>day</th>
      <th>time</th>
      <th>sensor_id</th>
      <th>sensor_name</th>
      <th>hourly_counts</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2887628</td>
      <td>2019-11-01T17:00:00.000</td>
      <td>2019</td>
      <td>November</td>
      <td>1</td>
      <td>Friday</td>
      <td>17</td>
      <td>34</td>
      <td>Flinders St-Spark La</td>
      <td>300</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2887629</td>
      <td>2019-11-01T17:00:00.000</td>
      <td>2019</td>
      <td>November</td>
      <td>1</td>
      <td>Friday</td>
      <td>17</td>
      <td>39</td>
      <td>Alfred Place</td>
      <td>604</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2887630</td>
      <td>2019-11-01T17:00:00.000</td>
      <td>2019</td>
      <td>November</td>
      <td>1</td>
      <td>Friday</td>
      <td>17</td>
      <td>37</td>
      <td>Lygon St (East)</td>
      <td>216</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2887631</td>
      <td>2019-11-01T17:00:00.000</td>
      <td>2019</td>
      <td>November</td>
      <td>1</td>
      <td>Friday</td>
      <td>17</td>
      <td>40</td>
      <td>Lonsdale St-Spring St (West)</td>
      <td>627</td>
    </tr>
    <tr>
      <th>4</th>
      <td>2887632</td>
      <td>2019-11-01T17:00:00.000</td>
      <td>2019</td>
      <td>November</td>
      <td>1</td>
      <td>Friday</td>
      <td>17</td>
      <td>36</td>
      <td>Queen St (West)</td>
      <td>774</td>
    </tr>
    <tr>
      <th>5</th>
      <td>2887633</td>
      <td>2019-11-01T17:00:00.000</td>
      <td>2019</td>
      <td>November</td>
      <td>1</td>
      <td>Friday</td>
      <td>17</td>
      <td>29</td>
      <td>St Kilda Rd-Alexandra Gardens</td>
      <td>644</td>
    </tr>
    <tr>
      <th>6</th>
      <td>2887634</td>
      <td>2019-11-01T17:00:00.000</td>
      <td>2019</td>
      <td>November</td>
      <td>1</td>
      <td>Friday</td>
      <td>17</td>
      <td>42</td>
      <td>Grattan St-Swanston St (West)</td>
      <td>453</td>
    </tr>
    <tr>
      <th>7</th>
      <td>2887635</td>
      <td>2019-11-01T17:00:00.000</td>
      <td>2019</td>
      <td>November</td>
      <td>1</td>
      <td>Friday</td>
      <td>17</td>
      <td>43</td>
      <td>Monash Rd-Swanston St (West)</td>
      <td>387</td>
    </tr>
    <tr>
      <th>8</th>
      <td>2887636</td>
      <td>2019-11-01T17:00:00.000</td>
      <td>2019</td>
      <td>November</td>
      <td>1</td>
      <td>Friday</td>
      <td>17</td>
      <td>44</td>
      <td>Tin Alley-Swanston St (West)</td>
      <td>27</td>
    </tr>
    <tr>
      <th>9</th>
      <td>2887637</td>
      <td>2019-11-01T17:00:00.000</td>
      <td>2019</td>
      <td>November</td>
      <td>1</td>
      <td>Friday</td>
      <td>17</td>
      <td>35</td>
      <td>Southbank</td>
      <td>2691</td>
    </tr>
  </tbody>
</table>
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<h4 id="Merge-Pedestrian-sensor-location-and-traffic-data">Merge Pedestrian sensor location and traffic data<a class="anchor-link" href="#Merge-Pedestrian-sensor-location-and-traffic-data">&#182;</a></h4>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#Add date column</span>
<span class="n">sensor_traffic</span><span class="p">[</span><span class="s1">&#39;date&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">sensor_traffic</span><span class="o">.</span><span class="n">date_time</span><span class="p">)</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">date</span>

<span class="c1">#Add day of week column</span>
<span class="n">sensor_traffic</span><span class="p">[</span><span class="s1">&#39;dow&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">sensor_traffic</span><span class="o">.</span><span class="n">date_time</span><span class="p">)</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">day_of_week</span>

<span class="c1">#convert sensor_id to integer</span>
<span class="n">sensor_traffic</span><span class="p">[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">]</span><span class="o">=</span><span class="n">sensor_traffic</span><span class="p">[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
<span class="n">sensor_location</span><span class="p">[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">]</span><span class="o">=</span><span class="n">sensor_location</span><span class="p">[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>

<span class="c1"># Mesh pedestrian sensor location and foot traffic datasets</span>
<span class="n">sensor_ds</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">sensor_traffic</span><span class="p">,</span> <span class="n">sensor_location</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">)</span>

<span class="c1">#Simply using the year to differentiate all the years prior to 2020 as before Covid, and post 2019 Covid traffic to now</span>
<span class="n">sensor_ds</span><span class="p">[</span><span class="s1">&#39;pre2020_hourly_counts&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">sensor_ds</span><span class="p">[</span><span class="s1">&#39;year&#39;</span><span class="p">]</span><span class="o">&lt;</span><span class="mi">2020</span><span class="p">,</span><span class="n">sensor_ds</span><span class="p">[</span><span class="s1">&#39;hourly_counts&#39;</span><span class="p">]</span> <span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">sensor_ds</span><span class="p">[</span><span class="s1">&#39;post2019_hourly_counts&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">sensor_ds</span><span class="p">[</span><span class="s1">&#39;year&#39;</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">2019</span><span class="p">,</span><span class="n">sensor_ds</span><span class="p">[</span><span class="s1">&#39;hourly_counts&#39;</span><span class="p">]</span> <span class="p">,</span> <span class="mi">0</span><span class="p">)</span>

<span class="c1">#Add column for day or night traffic</span>
<span class="c1">#Add column for day (5am to 5pm) or night (6pm to 4am) traffic</span>
<span class="n">sensor_ds</span><span class="p">[</span><span class="s1">&#39;day_counts&#39;</span><span class="p">]</span>   <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(((</span><span class="n">sensor_ds</span><span class="p">[</span><span class="s1">&#39;time&#39;</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">4</span><span class="p">)</span>  <span class="o">&amp;</span> <span class="p">(</span><span class="n">sensor_ds</span><span class="p">[</span><span class="s1">&#39;time&#39;</span><span class="p">]</span><span class="o">&lt;</span><span class="mi">18</span><span class="p">)),</span><span class="n">sensor_ds</span><span class="p">[</span><span class="s1">&#39;hourly_counts&#39;</span><span class="p">]</span> <span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">sensor_ds</span><span class="p">[</span><span class="s1">&#39;night_counts&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">sensor_ds</span><span class="p">[</span><span class="s1">&#39;day_counts&#39;</span><span class="p">]</span><span class="o">==</span><span class="mi">0</span><span class="p">,</span><span class="n">sensor_ds</span><span class="p">[</span><span class="s1">&#39;hourly_counts&#39;</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>

<span class="n">sensor_ds</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
     </code>
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<style scoped>
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        vertical-align: middle;
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    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>year</th>
      <th>mdate</th>
      <th>time</th>
      <th>sensor_id</th>
      <th>hourly_counts</th>
      <th>dow</th>
      <th>latitude</th>
      <th>longitude</th>
      <th>lat</th>
      <th>lon</th>
      <th>pre2020_hourly_counts</th>
      <th>post2019_hourly_counts</th>
      <th>day_counts</th>
      <th>night_counts</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>4.562230e+06</td>
      <td>4.562230e+06</td>
      <td>4.562230e+06</td>
      <td>4.562230e+06</td>
      <td>4.562230e+06</td>
      <td>4.562230e+06</td>
      <td>4.562230e+06</td>
      <td>4.562230e+06</td>
      <td>4.562230e+06</td>
      <td>4.562230e+06</td>
      <td>4.562230e+06</td>
      <td>4.562230e+06</td>
      <td>4.562230e+06</td>
      <td>4.562230e+06</td>
      <td>4.562230e+06</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>2.281708e+06</td>
      <td>2.017333e+03</td>
      <td>1.574675e+01</td>
      <td>1.146982e+01</td>
      <td>2.735892e+01</td>
      <td>4.926315e+02</td>
      <td>3.001057e+00</td>
      <td>-3.781341e+01</td>
      <td>1.449621e+02</td>
      <td>-3.781341e+01</td>
      <td>1.449621e+02</td>
      <td>3.986104e+02</td>
      <td>9.402113e+01</td>
      <td>3.575141e+02</td>
      <td>1.351174e+02</td>
    </tr>
    <tr>
      <th>std</th>
      <td>1.317918e+06</td>
      <td>3.580411e+00</td>
      <td>8.799595e+00</td>
      <td>6.938244e+00</td>
      <td>1.978492e+01</td>
      <td>7.475427e+02</td>
      <td>2.000297e+00</td>
      <td>6.212739e-03</td>
      <td>8.555391e-03</td>
      <td>6.212739e-03</td>
      <td>8.555391e-03</td>
      <td>7.386555e+02</td>
      <td>2.969237e+02</td>
      <td>7.097362e+02</td>
      <td>3.894963e+02</td>
    </tr>
    <tr>
      <th>min</th>
      <td>1.000000e+00</td>
      <td>2.009000e+03</td>
      <td>1.000000e+00</td>
      <td>0.000000e+00</td>
      <td>1.000000e+00</td>
      <td>0.000000e+00</td>
      <td>0.000000e+00</td>
      <td>-3.782402e+01</td>
      <td>1.449297e+02</td>
      <td>-3.782402e+01</td>
      <td>1.449297e+02</td>
      <td>0.000000e+00</td>
      <td>0.000000e+00</td>
      <td>0.000000e+00</td>
      <td>0.000000e+00</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>1.140558e+06</td>
      <td>2.015000e+03</td>
      <td>8.000000e+00</td>
      <td>5.000000e+00</td>
      <td>1.100000e+01</td>
      <td>4.300000e+01</td>
      <td>1.000000e+00</td>
      <td>-3.781863e+01</td>
      <td>1.449587e+02</td>
      <td>-3.781863e+01</td>
      <td>1.449587e+02</td>
      <td>0.000000e+00</td>
      <td>0.000000e+00</td>
      <td>0.000000e+00</td>
      <td>0.000000e+00</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>2.281116e+06</td>
      <td>2.018000e+03</td>
      <td>1.600000e+01</td>
      <td>1.100000e+01</td>
      <td>2.400000e+01</td>
      <td>1.770000e+02</td>
      <td>3.000000e+00</td>
      <td>-3.781381e+01</td>
      <td>1.449651e+02</td>
      <td>-3.781381e+01</td>
      <td>1.449651e+02</td>
      <td>5.400000e+01</td>
      <td>0.000000e+00</td>
      <td>2.400000e+01</td>
      <td>0.000000e+00</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>3.421673e+06</td>
      <td>2.020000e+03</td>
      <td>2.300000e+01</td>
      <td>1.700000e+01</td>
      <td>4.200000e+01</td>
      <td>6.010000e+02</td>
      <td>5.000000e+00</td>
      <td>-3.781102e+01</td>
      <td>1.449669e+02</td>
      <td>-3.781102e+01</td>
      <td>1.449669e+02</td>
      <td>4.350000e+02</td>
      <td>3.000000e+01</td>
      <td>3.500000e+02</td>
      <td>5.900000e+01</td>
    </tr>
    <tr>
      <th>max</th>
      <td>4.567701e+06</td>
      <td>2.022000e+03</td>
      <td>3.100000e+01</td>
      <td>2.300000e+01</td>
      <td>8.700000e+01</td>
      <td>1.597900e+04</td>
      <td>6.000000e+00</td>
      <td>-3.779432e+01</td>
      <td>1.449747e+02</td>
      <td>-3.779432e+01</td>
      <td>1.449747e+02</td>
      <td>1.597900e+04</td>
      <td>1.443700e+04</td>
      <td>1.161200e+04</td>
      <td>1.597900e+04</td>
    </tr>
  </tbody>
</table>
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<h3 id="Separate-day-and-night-data-sets">Separate day and night data sets<a class="anchor-link" href="#Separate-day-and-night-data-sets">&#182;</a></h3>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="n">flag_value</span><span class="o">=</span><span class="mi">0</span>
<span class="n">df_day</span><span class="o">=</span><span class="n">sensor_ds</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s2">&quot;day_counts &gt; @flag_value&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Day info</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">df_day</span><span class="o">.</span><span class="n">info</span><span class="p">(),</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>

<span class="n">df_night</span><span class="o">=</span><span class="n">sensor_ds</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s2">&quot;day_counts == @flag_value&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Night info </span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span><span class="n">df_night</span><span class="o">.</span><span class="n">info</span><span class="p">(),</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
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<pre><code class="language-python" data-lang="python">&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
Int64Index: 2455152 entries, 0 to 4562223
Data columns (total 27 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   id                      int64  
 1   date_time               object 
 2   year                    int64  
 3   month                   object 
 4   mdate                   int64  
 5   day                     object 
 6   time                    int64  
 7   sensor_id               int32  
 8   sensor_name_x           object 
 9   hourly_counts           int64  
 10  date                    object 
 11  dow                     int64  
 12  sensor_description      object 
 13  sensor_name_y           object 
 14  installation_date       object 
 15  status                  object 
 16  note                    object 
 17  latitude                float64
 18  longitude               float64
 19  direction_1             object 
 20  direction_2             object 
 21  lat                     float64
 22  lon                     float64
 23  pre2020_hourly_counts   int64  
 24  post2019_hourly_counts  int64  
 25  day_counts              int64  
 26  night_counts            int64  
dtypes: float64(4), int32(1), int64(10), object(12)
memory usage: 515.1+ MB
Day info
 None 

&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
Int64Index: 2107078 entries, 1 to 4562229
Data columns (total 27 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   id                      int64  
 1   date_time               object 
 2   year                    int64  
 3   month                   object 
 4   mdate                   int64  
 5   day                     object 
 6   time                    int64  
 7   sensor_id               int32  
 8   sensor_name_x           object 
 9   hourly_counts           int64  
 10  date                    object 
 11  dow                     int64  
 12  sensor_description      object 
 13  sensor_name_y           object 
 14  installation_date       object 
 15  status                  object 
 16  note                    object 
 17  latitude                float64
 18  longitude               float64
 19  direction_1             object 
 20  direction_2             object 
 21  lat                     float64
 22  lon                     float64
 23  pre2020_hourly_counts   int64  
 24  post2019_hourly_counts  int64  
 25  day_counts              int64  
 26  night_counts            int64  
dtypes: float64(4), int32(1), int64(10), object(12)
memory usage: 442.1+ MB
Night info 
 None 
</code>
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<p>Separate day and night, and before and after Covid for mapping</p>

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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#split dataset to map difference between time before covid-19 and time after covid-19</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df_day</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_day</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>

<span class="n">df_daypft_beforecovid</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;year&#39;</span><span class="p">]</span><span class="o">&lt;</span><span class="mi">2020</span><span class="p">]</span>
<span class="n">df_daypft_aftercovid</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;year&#39;</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">2019</span><span class="p">]</span>

<span class="c1">#get average hourly count for each sensor during the selected period of time</span>
<span class="n">df_daypft_beforecovid_avg</span> <span class="o">=</span> <span class="n">df_daypft_beforecovid</span><span class="p">[[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span><span class="s1">&#39;sensor_description&#39;</span><span class="p">,</span><span class="s1">&#39;lat&#39;</span><span class="p">,</span><span class="s1">&#39;lon&#39;</span><span class="p">,</span><span class="s1">&#39;hourly_counts&#39;</span><span class="p">]]</span>
<span class="n">df_daypft_beforecovid_avg</span> <span class="o">=</span> <span class="n">df_daypft_beforecovid_avg</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span><span class="s1">&#39;sensor_description&#39;</span><span class="p">,</span><span class="s1">&#39;lat&#39;</span><span class="p">,</span><span class="s1">&#39;lon&#39;</span><span class="p">,],</span><span class="n">as_index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span><span class="s1">&#39;hourly_counts&#39;</span><span class="p">:</span> <span class="s1">&#39;mean&#39;</span><span class="p">})</span>
<span class="n">df_daypft_aftercovid_avg</span> <span class="o">=</span> <span class="n">df_daypft_aftercovid</span><span class="p">[[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span><span class="s1">&#39;sensor_description&#39;</span><span class="p">,</span><span class="s1">&#39;lat&#39;</span><span class="p">,</span><span class="s1">&#39;lon&#39;</span><span class="p">,</span><span class="s1">&#39;hourly_counts&#39;</span><span class="p">]]</span>
<span class="n">df_daypft_aftercovid_avg</span> <span class="o">=</span> <span class="n">df_daypft_aftercovid_avg</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span><span class="s1">&#39;sensor_description&#39;</span><span class="p">,</span><span class="s1">&#39;lat&#39;</span><span class="p">,</span><span class="s1">&#39;lon&#39;</span><span class="p">,],</span><span class="n">as_index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span><span class="s1">&#39;hourly_counts&#39;</span><span class="p">:</span> <span class="s1">&#39;mean&#39;</span><span class="p">})</span>
     </code>
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<pre><code class="language-python" data-lang="python">         id                date_time  year     month  mdate       day  time  \
0   2887628  2019-11-01T17:00:00.000  2019  November      1    Friday    17   
12  2888288  2019-11-02T05:00:00.000  2019  November      2  Saturday     5   

    sensor_id         sensor_name_x  hourly_counts  ...  latitude   longitude  \
0          34  Flinders St-Spark La            300  ... -37.81538  144.974151   
12         34  Flinders St-Spark La              7  ... -37.81538  144.974151   

   direction_1 direction_2       lat         lon pre2020_hourly_counts  \
0         East        West -37.81538  144.974151                   300   
12        East        West -37.81538  144.974151                     7   

    post2019_hourly_counts  day_counts night_counts  
0                        0         300            0  
12                       0           7            0  

[2 rows x 27 columns]
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#split dataset to see difference between time before covid-19 and time after covid-19</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df_night</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>

<span class="n">df_night_beforecovid</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;year&#39;</span><span class="p">]</span><span class="o">&lt;</span><span class="mi">2020</span><span class="p">]</span>
<span class="n">df_night_aftercovid</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;year&#39;</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">2019</span><span class="p">]</span>

<span class="c1">#get average hourly count for each sensor during the selected period of time</span>
<span class="n">df_night_beforecovid_avg</span> <span class="o">=</span> <span class="n">df_night_beforecovid</span><span class="p">[[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span><span class="s1">&#39;sensor_description&#39;</span><span class="p">,</span><span class="s1">&#39;lat&#39;</span><span class="p">,</span><span class="s1">&#39;lon&#39;</span><span class="p">,</span><span class="s1">&#39;pre2020_hourly_counts&#39;</span><span class="p">]]</span>
<span class="n">df_night_beforecovid_avg</span> <span class="o">=</span> <span class="n">df_night_beforecovid_avg</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span><span class="s1">&#39;sensor_description&#39;</span><span class="p">,</span><span class="s1">&#39;lat&#39;</span><span class="p">,</span><span class="s1">&#39;lon&#39;</span><span class="p">,],</span><span class="n">as_index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span><span class="s1">&#39;pre2020_hourly_counts&#39;</span><span class="p">:</span> <span class="s1">&#39;mean&#39;</span><span class="p">})</span>
<span class="n">df_night_aftercovid_avg</span> <span class="o">=</span> <span class="n">df_night_aftercovid</span><span class="p">[[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span><span class="s1">&#39;sensor_description&#39;</span><span class="p">,</span><span class="s1">&#39;lat&#39;</span><span class="p">,</span><span class="s1">&#39;lon&#39;</span><span class="p">,</span><span class="s1">&#39;post2019_hourly_counts&#39;</span><span class="p">]]</span>
<span class="n">df_night_aftercovid_avg</span> <span class="o">=</span> <span class="n">df_night_aftercovid_avg</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span><span class="s1">&#39;sensor_description&#39;</span><span class="p">,</span><span class="s1">&#39;lat&#39;</span><span class="p">,</span><span class="s1">&#39;lon&#39;</span><span class="p">,],</span><span class="n">as_index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span><span class="s1">&#39;post2019_hourly_counts&#39;</span><span class="p">:</span> <span class="s1">&#39;mean&#39;</span><span class="p">})</span>
     </code></pre></div>
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<pre><code class="language-python" data-lang="python">        id                date_time  year     month  mdate     day  time  \
1  2887683  2019-11-01T18:00:00.000  2019  November      1  Friday    18   
2  2887738  2019-11-01T19:00:00.000  2019  November      1  Friday    19   

   sensor_id         sensor_name_x  hourly_counts  ...  latitude   longitude  \
1         34  Flinders St-Spark La            240  ... -37.81538  144.974151   
2         34  Flinders St-Spark La            158  ... -37.81538  144.974151   

  direction_1 direction_2       lat         lon pre2020_hourly_counts  \
1        East        West -37.81538  144.974151                   240   
2        East        West -37.81538  144.974151                   158   

   post2019_hourly_counts  day_counts night_counts  
1                       0           0          240  
2                       0           0          158  

[2 rows x 27 columns]
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<div class="usecase-section-header">Scenario</div>Examine Pedestrian Traffic</div>

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<p>Pedestrian traffic has decreased after 2019, the aim is to try to understand patterns of day and night traffic.</p>

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<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div>
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    <span class="icon"><i class="fab fa-python"></i></span>
    <span class=>Python</span>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#examine pre Covid and post 2019 foot traffic</span>
<span class="n">ds</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">sensor_ds</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s2">&quot;time&quot;</span><span class="p">])[</span><span class="s2">&quot;pre2020_hourly_counts&quot;</span><span class="p">,</span><span class="s2">&quot;post2019_hourly_counts&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;time&#39;</span><span class="p">])</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;#0f9295&quot;</span><span class="p">,</span><span class="s2">&quot;orange&quot;</span><span class="p">])</span>
<span class="n">axs</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Foot Traffic by Time&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">axs</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Average counts&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
     </code></pre></div>
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"
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  <div class="snippet-language">
    <span class="icon"><i class="fab fa-python"></i></span>
    <span class=>Python</span>
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     <div class=" highlight hl-ipython3"><code class="language-python" data-lang="python"><span></span><span class="c1">#distribution by traffic, by day</span>
<span class="n">pivot</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">sensor_ds</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;day_counts&#39;</span><span class="p">,</span><span class="s1">&#39;night_counts&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;year&#39;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">))</span>
<span class="n">rs</span> <span class="o">=</span> <span class="n">pivot</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">&#39;year&#39;</span><span class="p">,</span> <span class="n">ascending</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">rs</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;orange&#39;</span><span class="p">,</span><span class="s1">&#39;#056b8a&#39;</span><span class="p">],</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>

<span class="n">axs</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Foot traffic by year by day and night&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">axs</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Average counts&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
     </code></pre></div>
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945ESpV4ce3y+XybPOmTSp67itS3rErOn55LldTRdt9cWwiuTBcE/lYcfi9eJaEYsWzSZQMySXv06xZM6/uU9Uq6oX973//C41Gg+3bt6N169alrnv00UexcePGKq0rNDQUmZmZsFgsVx2wMzIy4HK5ygTs4ufu4vaOjY3FTTfdhKVLl+Lw4cMwmUzYv38/Ro4c6Tlhz1tjx47FU089hcWLF+O+++7DokWLEBsbi8TExFK3K65hz5496NChw2X3m5aWhtmzZ6Ndu3bYvHmz5+S8Yl9//XWF972a1ftSU1PL3V5RWz722GP45Zdf8Mknn2DatGn4+OOPodPpcO+993p1vOL9Xe64JW3fvh1Lly5FQkICVq9e7QlpAOB2uzF9+vRy93XrrbeiXr16+PTTTzF58uRKn8gIAO+99x4yMzOxYMGCMidrfv3115ec4cRbJdukbdu2Za6v6D2pKgUHBwOo+HmqaDtRTcZhIUQ+FhQUhGbNmuHs2bM4evRomevXr18PAKVmd+jcuTMAlLuE9LFjx3DmzBk0bdq0VE+OUqm8op6b4iB5pb0+x44dQ5s2bcoEa7fbjU2bNl3RPiujR48eEELgl19+uep9OZ1ObN68ucz24ueh+HkpqeSQjop6m71x3333wWAw4OOPP8aSJUuQnZ2NBx98sEzQ79GjBwDgzz//9Gq/J06cgNvtxvXXX18mWJ85cwYnTpyodK3eKO+PKrPZjN27d0On05V5vSQmJqJRo0ZYsGABfv31VyQlJeGOO+5AWFiYV8crHlqxe/fucodLVfS7BAA33XRTqWANAP/88w8sFku5x1Kr1XjooYdw9uxZLF++HPPnz0dgYCBGjx7tVa0lj108BKQkX/1BWvyeUt7+Tpw4UWpYi78Uz9axZcsWuN3uMtf76j3jat/XiHyJ4ZqoCjzwwAMQQuC5554r9WafkZHhmW7rgQceKHV7AJg6dSrS09M9210uF5599lm43W48+OCDpY4RHh6O9PT0CgNBRcLDwwEUjv+8Ek2aNMHRo0dLzZsrhMArr7yCgwcPXtE+K+OJJ54AADzzzDPljlGt7HjvF198sdTX8SaTCVOnTgWAcod6DBo0CC1atMDnn3+Ob7/9Fi1btrzk/NcVCQkJwahRo7Br1y7PNG8XT81XXENoaCheffVV/PPPP2Wud7vdpYJk8XjtTZs2lXrt5eXl4eGHH4bT6ax0rd748ssvS41RBwrnHzabzbjrrrs8Qy+KKRQKPPLII0hLS/O8/iszflmtVmP06NHIzc0ts6jT9u3bsXjx4jL3KW6bi4N3Wloaxo8ff8njPfLII1AqlXj88ceRnJyMUaNGlfnj5VIqOvaaNWswf/58r/dzKaNHj4Zarcb7779fah5xt9uN5557rtxwW9UaNWqE/v3749ixY6XG2AOFU0xWdl76ioSFhUGSpCt+XyPyJQ4LIaoCzz77LFavXo2ffvoJHTt2xI033oiCggJ89913SEtLw/PPP1/qRJ5evXrh+eefx/Tp09GuXTvcfvvtCAgIwOrVq7F//3706dMHzz33XKljDBo0CNu2bcOQIUPQt29faLVadOzYEcOHD79kbYMGDcI777yDhx9+GLfddhuCgoIQGhqKxx9/3KvHNnHiRIwdOxadO3fGbbfdBrVajb/++gsHDx7E8OHDsXz58so3WCVcf/31ePnllzF16lS0bt3aM891amoqNm3ahB49eni9cET9+vVhs9nQrl073HTTTXA4HPj+++9x/vx5jBs3rtwT6yRJwtixY/H0008DKAxdV2rcuHGYP38+zp49i+HDh6NBgwZlbhMeHo7vv/8et9xyC3r06IFBgwahbdu2kCQJp0+fxpYtW5CZmQmr1QqgcFztnXfeiW+++QadOnXC9ddfD7PZjN9++w06nQ6dOnXynGTmS0OHDkXv3r1xxx13oH79+ti0aRM2bdqEJk2aYNq0aeXe56GHHsJrr72Gs2fPon379ujZs2eljvnmm2/i999/x6xZs7B9+3bPPNdLlizBjTfeiJ9//rnU7bt27YrevXvjxx9/RK9evdCnTx+kpqZi9erVaNmyZbmrhBZr1KgRhg0b5tlnZb+tGDduHBYsWID//Oc/uP322xETE4P9+/fjl19+wR133IElS5ZUan/lKW7rZ555Bp07d8bIkSMREhKCNWvWIDs7Gx06dMDevXuv+jiVNWfOHPTu3Rvjxo3DqlWrPPNc//DDD7j55pvx008/lXuyY2UEBgaie/fu+PPPPzF69Gi0aNECSqUSN910k1fDqYh8SrZ5SohqIFRinliLxSLeeOMN0bZtW6HT6URgYKDo3bu3+Oqrryq8z9dffy169+4tAgMDhVarFW3atBFTp04VFoulzG3z8vLE2LFjRWxsrFAqlZWahmrGjBmiVatWQqPRCAClpvQrb/q7iy1YsEB07NhRGAwGER4eLkaMGCH27t1b4bR6vpyKr9jKlSvFDTfcIMLCwoRGoxENGjQQI0aMEL///vvlHr4Q4t+pDLOzs8W4ceNETEyM0Gg0olWrVuJ///vfJafVM5lMQqFQCJ1OJzIyMrw6XkU6deokAIgVK1Zc8nbJycli/PjxIj4+Xmi1WhEUFCRatmwp7r77brF06dJSt83PzxcvvfSSaNasmdBqtaJBgwZi3LhxIiMjo9w2LZ6Kz9tp60oq+fwVvy50Op2IiIgQY8aMEefOnbvk/UeMGHHJqQYv5/z58+L+++8XERERQqfTiY4dO4oFCxZU+JgyMzPFY489Jho3biy0Wq2Ii4sTL774osjPz7/s9JbLli0TAESXLl2uqNa//vpLDBgwQISGhnreD5YuXVphrZd6/RdPF3nxFHZCCPHVV1+Jzp07C61WKyIiIsTo0aPF2bNnvfrdLulS7VHe7+2lpsM7dOiQuOWWW0RISIgwGAyiR48eYsWKFeKdd94RAMq8hit7bCGEOHr0qEhMTBRGo1FIklRh+xBVNUmIi9YvJSKiS9qwYQMGDBiAu+++G19++eUV7yc3NxcxMTEwGo1ITk6+6t67msbtdiM+Ph6pqak4f/685+S36uqVV17Bq6++ivnz55cZpkVXZvTo0fjqq69w+PBhtGzZUu5yiHyibr2TExH5QPGsEt4OpanIRx99hLy8PIwbN67OBWsA+P7775GcnIx777232gfr3NxczJ07F0ajEXfddZfc5dQobre73JlKfv/9dyxZsgRt2rRhsKZahWOuiYi8sG/fPqxYsQI7duzA6tWrkZiYiO7du1d6P2azGR999BHOnj2LTz75BPXr1/fMQFJXTJs2DSaTCR9//DECAgLw4osvyl1ShVauXImdO3di+fLlSE1NxbvvvguDwSB3WTWK3W5Hw4YNMWDAALRq1QoqlQoHDhzAb7/9Bo1Ggzlz5shdIpFPcVgIEZEXFi5ciPvvvx/BwcG44YYb8OGHHyIiIqLS+0lJSUHTpk2h1Wpx7bXX4v333y81LWNdIEkS1Go12rRpg3feeQeDBw+Wu6QKjRkzBp9//jnq1auHBx54AFOnTq2T3zJcDZfLhQkTJmDdunU4c+YMCgoKEBERgb59+2LSpEnlTnlJVJMxXBMRERER+Qj//CYiIiIi8hGGayIiIiIiH6l1JzSWXDWOLi8iIgIZGRlyl1HnsN3lwXb3P7a5PNju8mC7+5+cbV7RwlPsuSYiIiIi8hGGayIiIiIiH2G4JiIiIiLyEb+OuR4/fjx0Oh0UCgWUSiWmTZuGvLw8zJw5E+np6YiMjMTEiRMRGBgIIQQWLFiAXbt2QavVYty4cYiLi/NnuURERER+J4SA1WqF2+2GJElyl1OtpaamwmazVdn+hRBQKBTQ6XRePxd+P6FxypQppZa5XbZsGdq3b48RI0Zg2bJlWLZsGe6++27s2rULFy5cwOzZs3H06FHMnz8fb775pr/LJSIiIvIrq9UKtVoNlarWzTvhcyqVCkqlskqP4XQ6YbVaodfrvbq97MNCtm3bhn79+gEA+vXrh23btgEAtm/fjr59+0KSJLRo0QL5+fnIysqSs1QiIiKiKud2uxmsqxGVSgW32+397auwlnK98cYbAIDBgwcjISEBZrMZYWFhAIDQ0FCYzWYAgMlkKrW0cHh4OEwmk+e2RERERLURh4JUP5V5Tvwarl9//XUYjUaYzWZMnTq1zPyAkiRV+gW1du1arF27FgAwbdq0UoGcLk+lUrHNZMB2lwfb3f/Y5vJgu8vDV+2emprKnutK8EdbabVar59bvz5zRqMRABASEoKuXbvi2LFjCAkJQVZWFsLCwpCVleUZj200GktNCp6Zmem5f0kJCQlISEjw/MzJ2yuHE97Lg+0uD7a7/7HN5cF2l4ev2t1ms1X5OOLKmjFjBgICAjB27Fi5SylFpVLB6XSW2rZ582ao1Wp07drVZ8ex2WxlnlvZF5GxWq2wWCyey3v37kWjRo3QpUsXbNy4EQCwceNGT0N06dIFf/zxB4QQSEpKgsFg4JAQqvncDmhMf0A6vwqSg+cQEBER+dqWLVuwY8cO2Y7vt55rs9mMd999FwDgcrnQp08fdOrUCc2aNcPMmTOxbt06z1R8ANC5c2fs3LkTTz75JDQaDcaNG+evUol8y+2ExrwF+rTl0KWvgtJZGKrrA3AYmsMe0g32kC6wB3eFS98E4Fg7IiKqBv73v//hu+++Q0REBGJiYtChQwcsXrwYixcvht1uR9OmTTF79my4XC4kJCTgzz//hFqtRm5uLgYPHuz5+WLJycmYNGkSMjMzoVQqMW/ePDRu3BhTp07F+vXrIUkSnnzySdx8883YvHkz5s6diy+++AIA8H//93/o0KEDRo4cie7du2PkyJFYs2YNnE4n5s2bB61Wiy+//BJKpRI//PADpk6dirS0NMycORMKhQLBwcH48ccfq7Td/Bau69Wrh3feeafM9qCgIEyePLnMdkmS8NBDD/mjNCLfEy5osrdCn14UqB0ZcCsDYA2/Htao4QgKbwTLqbXQmP+BPn0FAs4vBgC41JGwh3QtDNsh3eAIbAcoyr4xERFR3RB8dDLUeQd9uk9HYBvkNH/tkrfZu3cvfv75Z/z2229wOp0YMmQIOnTogKFDh2L06NEAgLfffhtff/01HnjgAfTs2RO///47hgwZgp9++glDhw4tN1gDwBNPPIHx48dj6NChsFqtEEJg1apVOHDgAH777TeYTCbceOON6NGjx2Ufi9FoxJo1a7Bw4ULMnTsX7777Lu65555SQ1gGDRqExYsXo379+p6JM6oSR8sT+YpwQ2PeBl36cujTV0JpT4NboYctPAGWqJtgNQ4AlIVzZAZGRCBPau25nyo/CZqcbdCYC//pM1YBANwKHRxBnYsCd1fYg6+FUIfI9QiJiKiO2Lp1K4YMGeKZ23nw4MEAgCNHjmD69OnIyclBfn6+ZzrlUaNG4cMPP8SQIUOwZMmScjtUASAvLw/nz5/H0KFDAQA6nQ4A8M8//2DEiBFQKpWIjIxEjx49sGfPHgQGBl6yzmHDhgEAOnTogNWrV5d7my5dumDixIkYPny457hVieGa6GoIAXXODujTl0OftgJK+wUIhQ5W40BYoobDFp4AoTRceh+SAs7AVnAGtkJBzD0AAIUttSho/wNNznYEnpoDCS4ISHAGtPL0bNuDu8Kla8ChJEREtdTlepj9beLEifj000/Rtm1bLFmyBFu2bAEAdO3aFadPn8bmzZvhdrvRqlUrnxxPpVJBCOH5+eLVGDUaDQBAqVTC5XKVu4+3334bO3fuxO+//46hQ4di9erV5U6S4SuyLyJDVOMIAXXObgQfew1Rf3dH5K6bEXD2S9iDOyGr9Rxc6LUXWe0+gTXqpssH6wq4tfVgjUpETvPXkHHtKlzocwgZHZcgt8kzcGmioE9dirBDT6De1h6ot6ULwg6MRcCZT6HO3Qu4nZc/ABER0SX06NEDa9asgcViQV5eHn777TcAhT3P9erVg8PhwNKlS0vd5/bbb8fjjz+OO+64o8L9BgYGon79+vjll18AFIZli8WC7t274+eff4bL5UJmZia2bt2KTp06ITY2FklJSbDZbDCbzdi0adNlaw8ICEBeXp7n55SUFFxzzTV47rnnEB4ejnPnzl1Jk3iNPddXSXLmIfjYFOQ1eRouXazc5VBVEQLqvP3QpS2HPn05VNZTEJIaNmM/5DZ9AdaI6yFUQVV3eFUA7GF9YA/rU7TBBVX+Yc8wEo15G/TpywEAboUBjuBrioaSdIM9+BoI1aW/ViMiIiqpffv2GD58OAYPHoyIiAh06tQJAPDcc88hMTER4eHh6Ny5c6kQe+utt+Kdd97BiBEjLrnv2bNn44UXXsC7774LlUqFefPmYejQodixYwcGDx4MSZLwf//3f4iKigIADB8+HAMHDkSjRo3Qrl27y9Y+ePBgPProo1izZg2mTp2KTz75BMnJyRBCoE+fPmjbtu0Vt4s3JFGyr70WqOq/Ri6myfoLxn33AZCQ2/Q55Mc+AChqzt8snAv1EoSAKv8g9MWB2pICIalgC+sLS2QirBFDrnj8c1W0u8J6FhrzdmhytkFr/geqvEOQ4IaAAs7A1rAVDSOxh3SBu47+IcjXu/+xzeXBdpeHr9q9oKAABsOVffMppxUrVmDNmjV4//33/XbM8ua5rgrlPScVzXNdc1JgNWUP6430rusRcvQlhBx/FfrUH2Fu+Q4cQe3lLo2ukCr/CPRpP0OXthxqy3EIKGEL64O8Ro/DEnEDhLrqxmldDbcuFlZdLKz1bgYASM5caHJ2FY7bNm+D4fwSBJ5dAABwamP/PUkypCucAa0AqXotWEBERDXHyy+/jPXr13umzKvLGK59wKVvCFP7L6BLX46Qo5MRseNG5Dd4ELlNnoNQBchdHnlBlX+scJaPtJ+hLkiCgAL20J7IbvgIrBFD4daEy11ipQlVEGzGvrAZ+xZucDuhzj/oOVFSm/03DGnLCq9SBsEefG3RiZJd4Qi+5orHixMRUd0zderUMtteeuklbNu2rdS2hx56CCNHjvRXWbLgsBAfkxxmBJ94EwHnF8GpjYW5+RuwRQyWtaZLqctfHSoLThTN8rEc6vxDEJBgD+kOS9RwWCOHwa2JrLJjV4t2FwJK65miGUm2QWPeDlX+YUgQEFDCEdQO9uCiWUlCusKtrSdvvT5QLdq9jmGby4PtLo+6PixEDhwWUgcIdQjMLd+GJfp2hBx5HuH7x8ASOQzm+NdrRTip6ZSWk0UrJS6HJm8/AMAW3BXm+NdhibwRbm20zBX6kSTBpW8Ii74hLNG3FW5ymKHJ2eE5SdJwfjECz34KAHDqGsEe0hW20N6wGfvz9UxERFQOhusqYg/pivQuaxB4+iMEpfwPWtMfyIl7sXAeY4kzIPqT0noGurQV0Kf/DE3uHgCAPfgamJtNgSUyEW5d+X951kVCHQJb+EDYwgcWbnDboc7bXxS2t0Nr2ghD6g8AAEdAG1jDB8Jm7A97cBeuJElERASG66ql0CCv8VOwRA5HaNIkhB59CYbUH5DdYjqcgb6ZXJ3Kp7Cegz59BfTpy6HJ2QkAsAd1hDnuv7BGJRYuvEKXp9DAEXwNHMHXIL/ho54ZVHSmDdBmrkfg6bkIOvUB3MpA2MKug83YHzbjAE5LSUREdRbDtR+4DHHI7LgE+tTvEXzsVUTuuAF5Dccit/EEz3LYdPUUtlTo01dCl/YztDmFJ1DYA9shp+mLsEQNh0vfWOYKawFJgjOwLfIC2yKv0XhIzlxos/6C1rQOWtMG6DMKl551GFp4grYtpBug1MlcOBERkX8wXPuLJMES/R/YjIMQfPw1BJ36APq0Fchu8RbsxbM5UKUp7OnQpa+EPm0FNOa/IUHAEdAaOU2fhyUyES5DM7lLrNWEKgjWyCGwRg4p7NUuOOYJ2gFnFyLwzMdwK/Swh/aC1TgANmN/uAxN5S6biIiu0DvvvIPu3bujb9+Ks8uMGTMQEBCAsWPHltpuNpuxdOlSjBkzpoqrvLTZs2fjySefrLL9M1z7mVtjRHbrWSiIvh2hSS8gYu9dKKh3K3KavVIjp3uThcsKffpyGC58B032Fkhww2Fogdwmz8AamQhnQHO5K6ybJAnOgOZwBjRHfsNHIbkKoMneDK1pA3Sm9Qg99jsAwKlv4gna9tDeEPz2hoioxnjuueeu+L45OTn44osvZA/X77//PsN1bWQP64O0Lr8j6NRsBJ76ELrMdTA3mwxL9B2AJMldXrWktJyC4dyXMJz/GkpnFpz6pshr/CQsUTfBGdBS7vLoIkJpgC08AbbwBOQAUBYke4K24fzXCDy7AELSwhba3TOExGloztc/EVEJb+3Zi8PZZp/us1VoCF7s2OGStzl9+jTuvvtudOvWDdu3b0d0dDQ+++wzvPjii0hISEBiYiJ+//13vPrqqzAYDOjatStOnjzpWUQmKSkJt99+O86ePYuHHnoIDz74IN58802cPHkSgwcPRt++ffHf//633GPPmTMHP/74IyRJwsCBA/HSSy9h//79mDRpEqxWKxo3bowZM2YgNDQUt9xyC15++WV07NgRJpMJQ4cOxdatW7FkyRL89ttvsFgsSElJwdChQ/Hyyy/jzTffhNVqxeDBg9GyZUtMnz4djz76KM6fPw+3242nnnoKN99881W1L8O1nJQ65DZ9HpaomxFy5AWEHXkahtTvkN3ibQ5nKCbc0JrWI+DsQmhN6wEoYI24AfmxY2AP7cUgVoO4DE1RYGiKggb3Ay4rtOZ/oDWth9a0ASHHXwOOvwanNvbfsdphfSBUQXKXTURUZyUnJ2POnDl455138Oijj2LVqlWe66xWK1544QX8+OOPaNSoEcaNG1fqvseOHcN3332H/Px8XHfddbj33nvx0ksv4ciRI/jtt98qPOa6deuwZs0arFixAnq9HllZWQCACRMm4PXXX0fPnj3xzjvv4L333sNrr712yfoPHDiANWvWQKPRoG/fvrj//vvx0ksvYcGCBZ4aVq5ciejoaHz55ZcACnvXrxbDdTXgDGiJzM4/wnB+MYKPv4mobQnIbfwk8hqNAxRaucuTheQwwXB+CQLOfQGV9RRcmijkNZ6A/JjRcGvry10eXS2lrsTqkVOgtJ6B1rSh8KTItJ8QcH4xhKSCPbgrbMb+sIYPgDOgDf+YIqI653I9zFWpYcOGaNeuHQCgQ4cOOH36tOe6Y8eOoXHjxmjUqBEAYMSIEVi0aJHn+kGDBkGr1UKr1SIiIgLp6eleHfPPP//EyJEjodcXDhkMCwtDTk4OzGYzevbsCQD4z3/+g0cfffSy++rTpw+Cg4MBAC1atMDZs2cRG1t6NqtWrVrhtddewxtvvIGEhAR0797dqzovheG6upAUKIi5B9bw6xFybAqCU96FPu0nmFu8DXvo1T/RNYU6ZxcCzn4OfdrPkIQNtpCeyIl7EdaIoZxHuRZz6RqgIOZuFMTcDbjt0OTsKBxCkrkOwclvITj5Lbg09WAz9iscrx12HYQ6TO6yiYhqNa323w4+pVIJq9V6xfd1uVw+ra3kvt1uNwCUqU+j0XguKxSKcldybNasGX755ResW7cO06dPR58+fTBx4sSrqomrmVQzbm09ZLWdi8z2X0ByWRCx+1aEHHkekiNb7tKqjssC/fkliNhxIyJ3JkKXsQoF9e9EWpffkdn5e1ijbmKwrksUGthDeyI37kWkd/0NF3ruQFbL92AP6QZdxhoYDz6G6L86IGLnTQhMmQl1zm5AuOWumoioTmnWrBlOnjzp6c3++eefL3ufgIAA5OXlXfI2ffv2xZIlS2CxWAAAWVlZCA4ORkhICLZu3QoA+OGHH9CjRw8Ahb3re/fuBVA4xMMbarUaDocDAHDhwgXo9XrcdtttGDt2LPbt2+fVPi6FPdfVlC18ENK7rUdQ8rsIODMfuoxfYY5/tTBo1pKvxpWWFASc/QKGC0ugcGbDYWiB7OZvwFLvNo61JQ+3NhqW+iNhqT8ScDuhzt1duIiNaT2CUmYgOOVduNRG2ML6FY7VNvaDWxMhd9lERLWaXq/Hm2++idGjR8NgMKBjx46XvY/RaETXrl0xcOBADBgwoNwTGgcMGIADBw5g6NChUKvVGDhwIF588UXMmjXLc0Jjo0aN8N577wEAxo0bh4cffhiLFy/GoEGDvKp99OjRSEhIQPv27XH77bdj6tSpkCQJarUab731VuUaohySEEJc9V6qkXPnzsldgs+pcvcjNOl5aHL3wGocAHPzN+HSN/LJviMiIpCRkeGTfXlFuKDNXIeAc59DZ1oPIalgjRhSeIJiSI9a84fD5fi93WsphT0T2qw/iubW3gilIxMAYA/sAFv4ANiMA2AP6gwoCvsR2O4+JgQkpxlK2zkobeeL/v17WWE9B5XkhCXoWthC+8Ae1oerd/oJX+vy8FW7FxQUwGAw+KCiqpWfn4+AgAAIIfDSSy+hadOmeOSRR/xag0qlKne4h6+V95zExMSUe1uG65pCuBBwdgGCkqcDwoXcJs8iv8FDVz1cwl9vwAp7JgwXvoHh3JdQWU/DpYlGfszdKKh/F9za6Co/fnXDD74qINxQ5+2HNrNwERtNzg5IcMOtCilcmj2sHwKiOyKrQAG32gi3OgxQaC6/37pKCEjO7DKBWWk959mmsJ2Hwm0pfTco4NZEwaWtD5e2PrRaHUTav3/4OPVNYAvtDVtYH9hDe3N+/yrC9xh51LVw/fHHH+O7776Dw+FAu3bt8M4773hORPQXhms/qLXhuojCehYhR1+GPvNXOALaILvldDiCO1/x/qr0DVgIqHN2IuDc59CnLYck7LCF9kJ+7BhYw6+v0+Oo+cFX9SRHNrRZfxbNrb0BSvuFMrdxKwPhVocVhW0j3Kqwop/DPAG81P+qsNqxlLsQkJxZ5QZmT69zRcFZW88TnF3amDKX3Zp6nm8KgKLXenoaVPlHoM3aBG32X9Bkb4HCVTju0hHQGraw3oU926E9OCTMR/geI4+6Fq6vxqFDh8os5KLVarFixYpK7Yfh2g9qe7gGAAgBXcZqhBz9LxT2VOTH3o/cpi9AqAIrvauqeAOWXBbo05bBcPZzaPL2wa0MREH0f1AQcy+cAS18eqyaih98fiYEVAXHEaa3IDcjBQqHCQpH1r//O7NKb3NVfMKNW2EoP3iX/F9Vehv8uQqlEIWPw1Y2MBdfLgzOpc+q/zc4lw3MxZfdmqhSwdkb5b7W3U6o8/YWhu2sv6DJ2Q7JbYWAEo7gjv/2bAd3qR1/zMiA7zHyYLj2P4ZrP6gT4bqI5MxB8Im3YTj3OdyaejA3fwPWyCGV2ocv34CVBScQcO4LGC58C4XTDEdAK+TH3AdLvVuvKPjXZvzgk4fX7e62FwXt4tBtKhvAS12fBYWr4oUH3Aod3GojRFHYdqmNEKpLhHO1EUKhL3sOQqngXFF4vgCpTHBWwqWtB3epwFwiOOti4FZHVjo4e8OrNndZC6dfLOrZVufshgQXhKSFPaSLp2fbEdSxSmqsjfgeIw9ftXvxWGa6PH+F6/Kek4rCNd+lajChCoa5xRsoqHcrQpNegPHAg7BEDIE5/nW4deU/4b4vwgVt5u8IOLsQuqyNRSco3lh0gmK3OnOCItUyCg3c2npwa+t5fx+3AwpndjkB3FT6sjMLGutpKBzZUDizK9ydUOjgVoUWBm5lAJT2dCht5yEJW+nbQQmXNhpubX04AjvAGn6DJzAXh2e3JgqQlFfYGH6g1MEe1hv2sN7IBSA5c6Exb4U26y9oszYhOHk6gOlwKwNhD+3h6dl2BrQCJM4oS7VP8ZzMKhVjWnXgdDqhUHj/XsNnrRZwhFyL9GtXI/DMxwhMeQ9R2wYgt+kLyI+9r8o+UBX2DBjOf114gqLtLFza+shp8hwK6o+CWxtVJcckqtYUarg1kXBrIr2/j9sJhdNcbgAv+bPkyoMjqAOskUPKGeMcWb2D8xUQqiDYwhNgC08AACjsJmiy/4I2uzBs6zLXAgBcaiPsob2LerZ7w6Vvyj/oqVbQ6XSwWq2w2WyQ+Jq+JK1WC5vNdvkbXiEhBBQKBXQ674eoMVzXFgo18hqNhyUyESFJLyLk2H+hT/0R2S3fhjOwrW+OIQTUOdsRcPYL6NNXFJ2g2Ac58a/CGj6YX9cSVZZCBbcmnDNmXIZbY4Q1ajisUcMBFJ7YXRi0C8O2Pn05AMCpjSkK231gC+sNt7a+nGUTXTFJkvw+60ZNVR2HQDEN1TIufWOYOiyGPm0Zgo9NQeT2ochr+CjymjwNcYUnVUmuAuhTlyLg3OdQ5x2AWxmE/Jh7ik5QjPfxIyAiujS3LhaW6Dtgib4DEAJKy4nCoJ29CdrMtTCkfgcAcOibwR5WFLZDe0KojTJXTkR1AcN1bSRJsNS7BVZjfwQffwNBpz+EPn0FzC3egs3Y3+vdKAuOI+Ds5zBc+A4KVw4cAa2R3eJtWKJugVDxRAsiqgYkCS5DMxQYmqEg9l5AuKHKO+gZQqJP/QEB576AgARHYFtPz7Y9pDvfx4ioSjBc12JCHQZzq3dhib4dIUeeR/je0SiIGoGc+FcqHhfqdkKXuRYB5xZCm/UnhKSGJTIRBbH3FU6LxbFfRFSdSQo4g9rBGdQO+Q0fBdwOqHN3e3q2A84uQOCZeRCSCvagzp6ebXvwNYBCK3f15RMuSC4rJLcVktsCyWUFPJctRdutpS4r8htDJRrDaWjOIXtEfsap+OoKtw2Bp+Yg6OT7EEo9cuJeRkH9OxERGYWMjAwo7OkwnP8KhnOLoLKdg1Mbg4KYewpXUKzMCVrkleo4RqwuYLv7X7Vrc5cFmpztnjm21bl7ClfyVOhgD+nm6dl2BLWv+ERRIYrCbXGoLQq6nsv/huCylwtDcOn7lx+QPfsVjit+uELSwhHYCo7AdoX/gtrBEdDav3Ov1yHV7vVeB8jZ5pznmgAAqvxjCEmaBK15C2wh3aFo8zQcx7+GPn0lJOGANawvCmLHwGocxN6OKsQ3YHmw3f2vure55MyBJvvvojm2N0OdfwgA4FYGw2lo9m/QLRWKbZBwZR+dboUOUOgglHoIhe7ffyV/VuogFPqLtuuLthdehrKC+xbdP9xgQ/7pP6HO3Q913n6o8w54pn4UUMBpiC8M2sWhO7AthDrUR61ad1X313ttVB3DNdNTHeMMiEdmp++gv7AEIcdfh2LLSCiVwciPvQ/5MffCZWgmd4lERH4jVMGwRVwPW8T1AACFPR2a7M3QZv0FpfUMhLJeqRALrwKxrvxArND6b2hdcAQs9SJhqXdr0QMVUNrOQp27ryhs74c2azMMqT967uLUNSzdwx3YrnA5ew4HJKoUhuu6SJJgqX8nbOEJCMdxZCjbQyi5zCoRkVsTCWvUzbBG3Sx3Kb4lSXDpGsClawBr5FDPZoU9ozBse3q490OfsdpzvUsdUSpsOwLbwaVvzMV7iC6B4boOc2siICJaQfArLCKiOsmtiYDN2L/UTFKSMxfqvIOesK3O3Q/t6bmQROES025lIByBbUuF7sITJ9UyPQqi6oXhmoiIiDyEKgj20O6wh3b/d6PbBnV+UqlhJYbzX0Fx1lJ4H0kLR0DLUj3czsA2V7y+AlFNxnBNREREl6bQwhHUvnAWlWLCBVVBMtR5+zw93Pr0VQg4/1Xh1VDAaWh20TjuthDqMJkeBJF/MFwTERFR5UlKOAPi4QyIh6XeLYXbhIDSdq70iZPmv2FIW+q5m1PboEQPd1s4gtrDrYnmiZNUazBcExERkW9IEly6WLh0sbBGDvFsVtgzoc47UNjLXXTypC5jjWdKQ5c6vKh3uz3yGzwKt4ZL1VPNxXBNREREVcqtCYfN2Bc2Y1/PNsmZB3X+odK93Kc+hNKWiuzWs+QrlugqMVwTERGR3wlVIOwhXWEP6erZFnroKegyfwXcdkChkbE6oivHiSqJiIioWrBEJkLhNEOb9afcpRBdMYZrIiIiqhZsxr5wK4OgT18hdylEV4zhmoiIiKoHhRbWiOuhy1hTODSEqAZiuCYiIqJq49+hIX/JXQrRFWG4JiIiomrDZuwHtzIIOg4NoRqK4ZqIiIiqD4UW1ojB0Gf8ArgdcldDVGl+n4rP7XZj0qRJMBqNmDRpEtLS0jBr1izk5uYiLi4OTzzxBFQqFRwOBz744AOcOHECQUFBmDBhAqKiovxdLhEREfmZJXI4DKk/Qpv9F2zG/nKXQ1Qpfu+5XrVqFWJjYz0/L1q0CMOGDcP777+PgIAArFu3DgCwbt06BAQE4P3338ewYcOwePFif5dKREREMrCF9YVbGQhdGoeGUM3j13CdmZmJnTt3YtCgQQAAIQQOHDiAHj16AAD69++Pbdu2AQC2b9+O/v37AwB69OiB/fv3Qwjhz3KJiIhIDkodrOGDoc9YzaEhVOP4NVwvXLgQd999NyRJAgDk5ubCYDBAqVQCAIxGI0wmEwDAZDIhPDwcAKBUKmEwGJCbm+vPcomIiEgm1shEKJzZ0GZvlrsUokrx25jrHTt2ICQkBHFxcThw4IDP9rt27VqsXbsWADBt2jRERET4bN91gUqlYpvJgO0uD7a7/7HN5VEr2j3sNogjTyE0dy1cLW6Tuxqv1Ip2r2GqY5v7LVwfOXIE27dvx65du2C322GxWLBw4UIUFBTA5XJBqVTCZDLBaDQCKOzFzszMRHh4OFwuFwoKChAUFFRmvwkJCUhISPD8nJGR4a+HVCtERESwzWTAdpcH293/2ObyqC3tHmpMgPb0UmQ0nAIo/D4HQ6XVlnavSeRs85iYmHK3+21YyKhRozB37lzMmTMHEyZMQLt27fDkk0+ibdu2+PvvvwEAGzZsQJcuXQAA1157LTZs2AAA+Pvvv9G2bVvPcBIiIiKq/ayRiVA6s6Dh0BCqQWSf53r06NFYsWIFnnjiCeTl5WHgwIEAgIEDByIvLw9PPPEEVqxYgdGjR8tcKREREfmT1dgfboUBei4oQzWIJGrZFBznzp2Tu4QahV9hyYPtLg+2u/+xzeVRm9o97MBj0GRvQmrPXdV+aEhtaveaok4PCyEiIiKqLEtUIpQOEzTmLXKXQuQVhmsiIiKqtmzGgXAr9NBzQRmqIRiuiYiIqNoSSj1s4QnQZawG3E65yyG6LIZrIiIiqtYskYlQOjKhMW+VuxSiy2K4JiIiomrNFj6ocGgIZw2hGoDhmoiIiKq1wqEhg6BLXw0Il9zlEF0SwzURERFVe5bIYVA60qHJ5tAQqt4YromIiKjas4UnwK3QcWgIVXsM10RERFTtCaUBNuNA6NJXcWgIVWsM10RERFQjFC4okw6N+R+5SyGqEMM1ERER1Qg2YwKEQscFZahaY7gmIiKiGkGoAmA1DoQuYxUg3HKXQ1SuKw7XFy5cgN1u92UtRERERJdkiUyE0p4GjXmb3KUQlcurcP3VV19hw4YNAAAhBF5//XU89dRTePTRR3H06NGqrI+IiIjIwxY+CEKhg46zhlA15VW43rRpE2JiYgAAu3btQkpKCt544w307dsXX331VZUWSERERFRMqAJhNfaHPp1DQ6h68ipcm81mhIeHAygM1z179kR8fDyGDh2K5OTkKi2QiIiIqCRrZCKU9gvQmLfLXQpRGV6F68DAQKSnpwMA9u7di/bt2wMAXC4XhBBVVx0RERHRRazhgyEkLYeGULWk8uZG3bt3x+zZs1G/fn3k5eWhY8eOAICUlBRER0dXaYFEREREJf07NGQlcuJfASROfkbVh1fh+r777kNkZCQyMjJw9913Q6fTAQCysrJw/fXXV2mBRERERBezRiVCn7kGmpwdsId0lbscIg+vwnVWVhaGDRsGhaL0X4bDhg1DZmZmlRRGREREVJGSQ0MYrqk68ep7lPHjxyM3N7fM9ry8PIwfP97nRRERERFdilAFwWrsB336Ss4aQtWK14OUJEkqs81qtUKj0fi0ICIiIiJvWCOHQWk7D3XOTrlLIfK45LCQzz77zHP5q6++KhWk3W43jh8/jiZNmlRZcUREREQVsUZcDyFpoE9fAUdIF7nLIQJwmXB9+vRpz+WzZ89Cpfr35iqVCk2bNsXw4cOrrjoiIiKiCghVMGzGvtClr0ROs8mcNYSqhUuG6ylTpgAAPvzwQ4wZMwYGg8EvRRERERF5wxKZiLDMtVDn7IIj5Fq5yyHybsz1uHHjGKyJiIio2rGGXw8hqaHngjJUTXg1FZ/dbseqVauwf/9+mM3mMqsyvvvuu1VSHBEREdGlCHUIbGElh4aUnYCByJ+8Ctfz58/Htm3b0KNHD7Ro0aLcmUOIiIiI5GCJSkTY4d+hzt0NR3BnucuhOs6rcL1t2zZMnDgRHTp0qOp6iIiIiCql5NAQhmuSm1djrrVaLSIiIqq6FiIiIqJKE+pQ2MKugy5tBXDR0FUif/MqXN90001YsWJFmbHWRERERNWBJTIRKtsZqHP3yF0K1XFeDQvZu3cvDh8+jN27d6NBgwZQKpWlrn/hhReqpDgiIiIib1gjrodIUhUNDekkdzlUh3kVroODg9GtW7eqroWIiIjoigh1WOHQkPQVyIn7P84aQrLxKlyPGzeuqusgIiIiuiqWyESEHXkG6ty9cAR3lLscqqO4TigRERHVCtaIGyAkFXRcUIZk5FXP9TPPPHPJua25iAwRERHJTajDYAvtA336SuTGvcShISQLr8J1jx49Sv3scrmQkpKCI0eO4IYbbqiSwoiIiIgqyxo5DKFJz0Gdtx+OoPZyl0N1kFfh+j//+U+523/++Wekp6f7tCAiIiKiK2WNGAKRNAm69BUM1ySLqxpz3a1bN2zatMlXtRARERFdFbfGCFtYb+i5oAzJ5KrC9aFDh6DRaHxVCxEREdFVs0YmQmVNgSrvgNylUB3k1bCQt99+u9TPQghkZ2cjOTm5wiEjRERERHKwRgyFSHoR+vTlyA1qJ3c5VMd4Fa6DgoJK/SxJEho2bIi77roLHTtyHkkiIiKqPtwaI+xhvaBPX4HcppM4awj5FReRISIiolrHEpmI0KQXoMo/CGdgW7nLoTqkUmOuU1NTsWPHDuzcuROpqalVVRMRERHRVbFGDIGAovDERiI/8qrnuqCgAHPnzsXWrVs9i8kIIdC9e3c89thj0Ov1VVokERERUWW4NRGwh/YsGhryPIeGkN941XO9cOFCnDx5ElOmTMGiRYuwaNEiTJ48GadOncLChQuruEQiIiKiyrNEJkJlOQFV/iG5S6E6xKtwvX37dowdOxZt2rSBSqWCSqVC27Zt8cgjj2Dbtm1VXSMRERFRpVkjbywcGpLOoSHkP16Fa7vdXmbGEAAIDAyEw+HweVFEREREV6twaEgP6LigDPmRV+G6ZcuW+Oabb2Cz2TzbrFYrvv32W7Ro0aLKiiMiIiK6GpbIRKgtx6HKPyx3KVRHeHVC43333Yc33ngDY8eORaNGjQAAp06dglarxf/93/95dSC73Y4pU6bA6XTC5XKhR48euOOOO5CWloZZs2YhNzcXcXFxeOKJJ6BSqeBwOPDBBx/gxIkTCAoKwoQJExAVFXXlj5SIiIjqHGvkjRBHXy48sTGwtdzlUB0gCeHd9yQ2mw1//vknzp07BwCIjY3Fdddd5/Xy50II2Gw26HQ6OJ1OTJ48GWPGjMGKFSvQvXt39O7dGx9//DGaNGmC66+/HmvWrMHJkyfxyCOP4K+//sI///yDiRMnXvY4xfWRdyIiIpCRkSF3GXUO210ebHf/Y5vLg+1eWvju26GwZyC924YqPQ7b3f/kbPOYmJhyt3s9z7VWq0VCQgLuvfde3HvvvRg0aJDXwRooXNVRp9MBAFwuF1wuFyRJwoEDB9CjRw8AQP/+/T0nSG7fvh39+/cHAPTo0QP79++Hl38HEBEREXlYIhOhLjgKVf4RuUuhOsCrYSFff/01wsPDcf3115fa/uuvv8JkMuHOO+/06mButxsvvPACLly4gBtuuAH16tWDwWCAUqkEABiNRphMJgCAyWRCeHg4AECpVMJgMCA3NxfBwcGl9rl27VqsXbsWADBt2jRERER4VQsVUqlUbDMZsN3lwXb3P7a5PNjuFwm8G+LoywjPWwdX495Vdhi2u/9Vxzb3Klz/8ccfePrpp8tsj4uLw7Jly7wO1wqFAu+88w7y8/Px7rvv+mQIR0JCAhISEjw/8+uYyuFXWPJgu8uD7e5/bHN5sN0vpkJ4SHcoTn6LjHqPVdlR2O7+V2OHheTk5JTpMQaAoKAgmM3mShcTEBCAtm3bIikpCQUFBXC5XAAKe6uNRiOAwl7szMxMAIXDSAoKCsqdDpCIiIjocgqHhiRBlZ8kdylUy3kVriMiInDoUNnVjQ4dOuQJw5eTk5OD/Px8AIUzh+zduxexsbFo27Yt/v77bwDAhg0b0KVLFwDAtddeiw0bNgAA/v77b7Rt29az9DoRERFRZRQuKCNBxwVlqIp5NSwkISEBn3/+OZxOJ9q1awcA2L9/P7766ivcfPPNXh0oKysLc+bMgdvthhACPXv2xLXXXosGDRpg1qxZ+Oabb9C0aVMMHDgQADBw4EB88MEHeOKJJxAYGIgJEyZc2SMkIiKiOs+trQd7SDfo01Ygr0nZoa5EvuL1VHxfffUVVq5cCafTCaBwAPmNN96I0aNHV2mBlcWp+CqH48PkwXaXB9vd/9jm8mC7ly/gzGcIOfZfpHXdAGdAc5/vn+3uf9VxzLVXPdcAMGrUKNx66604c+YMAKBBgwaeqfWIiIiIqjtL5FCEHPsvdOkrkBdw+bUziK6E1/NcA4BOp0N8fDzi4+MZrImIiKhGcWvrwxbcFfr0lXKXQrVYpcI1ERERUU1mjUqEOv8QlAXH5C6FaimGayIiIqozLBE3AgD0aZw1hKoGwzURERHVGW5dDOzBXaDnlHxURRiuiYiIqE6xRBYPDTkudylUC3kdrk+dOoVPP/0Ub775JrKysgAA//zzD5KTk6usOCIiIiJfs0QOAwD2XlOV8Cpc79mzBy+++CJMJhP2798Pu90OAEhNTcV3331XpQUSERER+VLh0JBrOe6aqoRX4XrJkiW499578dxzz0Gl+ndq7LZt2+L4cX6lQkRERDWLJXIY1PkHoSw4IXcpVMt4Fa5Pnz6Nzp07l9keGBiIvLw8nxdFREREVJWskYkAwDmvyee8CteBgYEwmUxltp84cQJGo9HnRRERERFVJZcuFvagztBx3DX5mFfhunfv3li0aBEyMzMhSRJcLhcOHjyIL7/8Ev369avqGomIiIh8zhKVCE3efigtKXKXQrWIV+H6zjvvRFRUFMaNGwer1Yqnn34ar776Klq1aoVbb721qmskIiIi8jnP0BCe2Eg+pLr8TQCVSoUnn3wSd9xxB1JSUuB2u9G0aVPUr1+/qusjIiIiqhIuXQPP0JC8xo/LXQ7VEl6F62LR0dGIjo6uqlqIiIiI/MoSmYiQE69DaTkJl76x3OVQLeBVuP7www/L3S5JEtRqNaKjo9GrVy+e3EhEREQ1ijXyRoSceB369BXIazRe7nKoFvAqXOfk5ODw4cOQJAmNGjUCULhiIwDExcXhn3/+wbfffovXXnsNTZo0qbJiiYiIiHzJpW8Ee1BH6NJXMlyTT3gVrlu1agWdTofHHnsMWq0WAGCz2TBv3jw0btwYL774Ij744AN88cUXmDx5cpUWTERERORL1shEBJ94A0rLKbj0jeQuh2o4r2YLWbVqFW6//XZPsAYArVaLW2+9FStXroRKpcLNN9+MlJSUqqqTiIiIqEpYimYN0XFBGfIBr8K11WpFdnZ2me3Z2dmwWq0AAL1eD5fL5dPiiIiIiKqaS98I9sAO0HNBGfIBr8J1t27d8NFHH2HLli1IS0tDWloatmzZgo8++gjdunUDABw7dgwxMTFVWiwRERFRVbBGJUKTuxtKy2m5S6Eazqsx1w8//DA+//xzvP/++57eaaVSiQEDBuCee+4BADRo0ACPPvpo1VVKREREVEUskcMQfOJN6NJXIr/RWLnLoRrMq3Ct1WrxyCOP4N5770VqaioAoF69etDpdJ7bcJYQIiIiqqlc+iawB7aDPn0FwzVdFa+GhRTT6XRo3LgxGjduXCpYExEREdV01shEaHJ3QWk9I3cpVIN5vULj/v378ddffyEjIwNOp7PUdVOmTPF5YURERET+ZIlMRHDytMKhIQ051JWujFc91xs2bMBbb70Fi8WCgwcPIjg4GPn5+UhOTkaDBg2qukYiIiKiKucyNIUjsC1nDaGr4lW4Xr58OR544AFMmDABSqUSo0aNwvTp03HddddxeAgRERHVGpbIRGhydkJhPSt3KVRDeRWuU1NT0b59ewCAWq32zG09ZMgQbNiwocqKIyIiIvKn4gVl9FxQpvoTbiD/pNxVlOFVuA4KCvIEaqPRiNOnC+eAzM3Nhd1ur7rqiIiIiPzIZYiDI6ANh4ZUd24HQg9PgHpdbyjs6XJXU4pXJzS2atUKe/bsQaNGjdCzZ08sWLAAe/fuxb59+9ChQ4eqrpGIiIjIbyxRiQhOng6F9Szculi5y6GLSK4ChB14FDrTOjjbvgq3OkLukkrxKlw/+OCDnh7qESNGQKFQ4MiRI+jZsyduu+22Ki2QiIiIyJ8skcMQnDwd+vRVyG/4sNzlUAkKuwnGffdCnbsH2S2mw9D6KSAjQ+6ySrlsuHa5XPjrr7/QtWtXAIBCocCIESOqui4iIiIiWbgM8XAEtC6ako/hurpQWs/AuGcUVLazyGr7CayRQ2CQu6hyXHbMtVKpxKJFizzLnhMRERHVdpbIYdDmbIPCdl7uUgiAKu8wInbeDKU9HZkdvoI1cojcJVXIqxMamzdvjhMnTlR1LURERETVgjVyOABAn75K5kpIk/0PInbfCgDI6Pwj7KHdZa7o0rwacz1o0CB8+eWXyMjIQFxcHLRabanr4+LiqqQ4IiIiIjk4A+LhCGgFXfoK5Dd4UO5y6ixtxq8wHnwMTm0sTB2/gktX/Rcv9Cpcz549GwDwxRdflHv9kiVLfFcRERERUTVgiUxEUMoMKGwX4NZGy11OnWM4/zVCjjwPR1BHmNp/AbfGKHdJXvEqXH/wwQdVXQcRERFRtWKNTERwyruFs4Y0eEDucuoOIRB46n0EJ78Na1h/ZLX9GEIVIHdVXvMqXEdGRlZ1HURERETVijOgORyGFkVDQxiu/UK4EXxsCgLPfoaCerciu+V7gEItd1WV4tUJjQCwa9cuTJs2DRMnTkRG0XyCv//+O/bt21dlxRERERHJyRqZCI35HyhsqXKXUvu5bQg7OB6BZz9DXoNHkN3qfzUuWANehus///wTM2fORHR0NNLS0jzT8rndbvz8889VWiARERGRXCxRiZAgoMtYLXcptZrkzEP43nuhT/8Z5rj/Iid+CiB53QdcrXhV9c8//4xHH30UY8aMgVKp9Gxv3rw5UlJSqqo2IiIiIlk5A1rCYWgOfdoKuUuptRT2dITvvh2a7C3IajUL+Y3Gyl3SVfEqXJ8/fx4tWrQos12n06GgoMDnRRERERFVF4VDQ/6GwpYmdym1jtJyEhE7R0BVcBSm9gtgif6P3CVdNa/CdVhYGM6fL7tC0aFDhxAdzalpiIiIqPayRBYPDeGCMr6kyt2PiJ03Q+HMRmbHb2ELHyR3ST7hVbhOSEjAggULcPjwYQBAZmYmNmzYgEWLFmHw4MFVWiARERGRnAqHhsRDn86hIb6iyfoLEbtvg1CokdF5GRwh18pdks94NRXfzTffjIKCAkydOhUOhwOvvvoqVCoVhg8fjiFDqu/a7kRERERXTZJgjUxE4MnZUNjT4dZwiuKroUtbgbBDT8Cpb4LMDovh1sXIXZJPeRWuAeCuu+7CrbfeijNnzkAIgQYNGkCn01VlbURERETVgiVyGIJOzoIufRUKYu+Tu5way3D2c4Qc/T/Yg7vA1H4BhDpM7pJ8zqthIStXroTZbIZWq0WzZs0QHx/PYE1ERER1hjOgNZz6OA4NuVJCICj5XYQefQm28ASYOn5dK4M14GXP9YoVK7Bo0SK0a9cOffv2Rbdu3aDVaqu6NiIiIqLqQZJgiUxE4KkPoLBnwK2JkLuimkO4EJL0IgLOL0Z+9J0wt3gbUHg9eKLG8eqRffjhhzhw4AA2bdqEzz77DJ988gm6dOmCvn37okOHDlAoLt8BnpGRgTlz5iA7OxuSJCEhIQE33ngj8vLyMHPmTKSnpyMyMhITJ05EYGAghBBYsGABdu3aBa1Wi3HjxiEuLu6qHzARERHRlbBEJSLo1GzoMlajIOYeucupGVxWhB16HPqM1cht9Dhym04CJEnuqqqUV+FakiS0a9cO7dq1w0MPPYQdO3Zg06ZNePfddxEQEIB58+Zddh9KpRL33HMP4uLiYLFYMGnSJHTo0AEbNmxA+/btMWLECCxbtgzLli3D3XffjV27duHChQuYPXs2jh49ivnz5+PNN9+86gdMREREdCWcAW3g1DeFPm0Fw7UXJIcZxv0PQGv+G+b415Df4EG5S/KLSq8rqVKp0KJFC7Ro0QKRkZHIzs726n5hYWGenme9Xo/Y2FiYTCZs27YN/fr1AwD069cP27ZtAwBs374dffv2hSRJaNGiBfLz85GVlVXZcomIiIh8o2hoiCZ7MxT2TLmrqdYUtguI2H0bNDk7YGr9YZ0J1kAlZguxWCz4+++/sWnTJhw8eBDR0dHo3bs3+vbtW+mDpqWlITk5GfHx8TCbzQgLKxzQHhoaCrPZDAAwmUyIiPh3PFN4eDhMJpPntkRERET+ZolMRNCp94uGhtwtdznVkrLgOML3jILCmYXM9l/Abqx8VqzJvArXM2bMwK5du6DX69GrVy/cddddiI+Pv6IDWq1WzJgxA2PGjIHBYCh1nSRJkCo5Dmft2rVYu3YtAGDatGmlAjldnkqlYpvJgO0uD7a7/7HN5cF2r0Lh/SAOxyE4ew0MHSaUuortDkim7VDtvhWQJDj7/YZgY9UuDlMd29yrcK1Wq/HMM8+gY8eOZU5e3Lt3Lzp06ODVwZxOJ2bMmIHrrrsO3bt3BwCEhIQgKysLYWFhyMrKQnBwMADAaDQiIyPDc9/MzEwYjcYy+0xISEBCQoLn55L3ocuLiIhgm8mA7S4Ptrv/sc3lwXavWkHhNyLw1EcwnUuCW/NvNqnr7a41bUTY/ofg0kQgs8NiuNyNgSpuDznbPCam/MVvvBpz/eSTT6Jz586eYG0ymfDDDz/g8ccfxxtvvOFVAUIIzJ07F7GxsUhMTPRs79KlCzZu3AgA2LhxI7p27erZ/scff0AIgaSkJBgMBg4JISIiItlZIxMhwQVdxi9yl1Jt6FOXwrjvXrj0TZDReRlchro7w5vXY67dbje2bduGdevWYe/evWjUqBEGDx6Mnj17enX/I0eO4I8//kCjRo3w3HPPAShc9XHEiBGYOXMm1q1b55mKDwA6d+6MnTt34sknn4RGo8G4ceOu4OERERER+ZYjsB2cusbQpa9AQcwoucuRXcDpTxBy/BXYQnrC1P4zCFWw3CXJ6rLh+ty5c/j999/xxx9/QKvVok+fPti7dy+eeOIJNGjQwOsDtWrVCt9++225102ePLnMNkmS8NBDD3m9fyIiIiK/KF5Q5vRcSA4ThLrssNU6QQgEnXgLQafnwBJxI7Javw8ouYL3JcP15MmTcfr0aXTv3h0TJ05EmzZtAAA//fSTX4ojIiIiqo6sUYkIOj0H+ow1KKh/l9zl+J/bgdAjz8GQ+h3yY+6BufkbgKSUu6pq4ZJjrpOSktC3b18MGzbME6yJiIiI6jpHYHs4dY2gS1shdyl+J7ksMO5/EIbU75DT5BmYm7/FYF3CJcP1W2+9BZfLhcmTJ+P555/HihUrvF40hoiIiKjWkiRYI4dBm70JkqPuLHInOUwI3zMSWtN6ZDd/C3lNnq71y5lX1iXDddOmTfHQQw9h3rx5SExMxI4dO/DYY4/B7XZj586dyMvL81edRERERNWKJTIRknBCl7FG7lL8QmE9i4hdt0Kduw9ZbeehIPZeuUuqlryaLUSj0aBv377o27cvLly4gN9//x0rV67EN998g3bt2uGll16q6jqJiIiIqhVHUEc4dQ2hT18BS/075S6nSqnykxC+dxQkZx4yOyyGPayX3CVVW17Nc11SdHQ0Ro8ejY8++ggTJ06ESuX1bH5EREREtUfx0JCsPyE5suWupsqozdsQsesWQLiQ0fkHBuvLuOJkrFAo0LVrV8+iL0REVDsIIWBzu2FxOlHgdMHicsJS4v8CpxMFLhcsTicsxf87XWW2FThdUKqUCFYqEaHVIlynRbhWhwhd4eWIossBKhUkjtmkGqp4Sj5dxhqgfrzc5ficNuM3hB0cC7c2GpkdvoZL30jukqo9djvXUSm5eXj/4CHsMJkwuH59jG4WhyZBgXKXRUReKh2Ai8PuReG2RNgtKArAF28vDs0FJf63Op1wV6IWCYBepYJeqYRBpYReqYJepYRBpYJGo8G53Fzsy8qCyWord786pRLhWm1h6NZqEa4rDN0RWi0idLqi63QILwriRNWJI6gTnNoG0KevADBe7nJ8Sn9+CUKPPAdHYFuYOnwJtyZC7pJqBL5L1TGpFgs+PHQEP6achEahQO/YGCw5kYzFx0+gT70ojG4Wh+ui60HBXiQi2QghcCa/ALtNJuwxmXA4Owd5DkeJEFwYjkUl9lkcgIvDb8kQbNRqCq9TKv+9TVFYLrm9+Ge9SgmD8t/baRWKCnueIyIikJGRAQBwCYFsmx2ZNisyrDZk2GzIsFqRabUhw1b4/5n8AuwxZcFks5X7+PRKZVEI13nCeHHwLg7jxdv1DOLkD0VDQwLOfgan+QAkVxCE0iB3VVdHCASemoPg5LdgDeuLrLafQKjYAectvvPUEdk2O+YnJWHxsRNwCYGRcU3waKuWaN2gAQ6dOYPvklPwzYlkPLb5bzQKCMCoZk1xS5PGCFKr5S6dqNYrcDpxICsbu00m7M40Ya8pC5k2GwDAoFKhdWgIGgYGXBR2ywZgQ1HwLQ7Nei8DsL8oJalwaIhOixYhl76t0+1Glt1eGLytVmTabEWBvDCYZ1ptSMnLw46MTGTZ7eXuI0Cl8vSI/9sDXv7QFK2yes3RK4SAG4DL7YZTCLiEgMst4BRuuISA0120TRRdX/SzU7jhcgs0VChgcDgQwPdwv7BE3YTAM/Og/u0a1AfgVgbBpYmCWxsFlyYabk0UXNp6cGvqwVXicrUMrMKN4OOvIvDMfBREjUB2q5mAQiN3VTWKJISoTOdHtXfu3Dm5S6hW8p1OfHnsOD47chT5TieGN2qIx9u0QoOAAACle5Xsbjd+O3sOi4+dwG6TCXqlEjc3boRRzZoiPjhYzodR65Rsd/Kf6tDuQgicys/HnkwT9piysNtkQpI5B66it+ImgYHoFB6GjkYjOhqNaB4SDGUN/ibJH23ucLuRZSvuCbch02pFhs1WKpinW23ItFlhtjvK3UegSlWqBzxcp0WgSl0qsBZeLgy0hUG3cJtbCDjd7qKwWxx83f/evkQo/vd+5YRkz2XheT1crRCNGrEGA2IMBs//MQEGxBj0iDUYEKxhaPIVtXkbwpQZKMg8DoU9DUr7BShsaVDaU6G0p0FyW8vcx60wwK2tB5emnieAl7xcHMaFKtg/c0m77Qg9PBGGtGXIi30QOfGvAFKl577wKznf12NiYsrdznBdS9ndbnx3IgVzDx9Bps2GAfWj8VTb1mgRUrq7qKIX5YGsbCw+fhyrTp+F3e1Gj8hIjI6PQ//60TX6g766qA4hry6So93znU7sLwrRe01Z2GMywWQr7GkNUKnQPiysVJgO1dausFPdXut2txsmq62oJ9x60dCUomBedH2+0wmVJEGpUBT+L0lQKiSoJAUUEqCUirYrCq9TSYqi64tuKymgUhRflqBSKKCUJCgkqcT9yu7738uF1xfe99/9KC8+Tsn6iv4JnQ5HUlNxNr8A5wosOFtQgPMFBbC4XKXaI0itKgzcJQN4gB6xhgDEGPQI1Whk/8ajJqnw9S4EJGcOlPZUKOypUBaF7uLLCntq4c+2VCjcljJ3dyt0nqB9cRgv2RsuVKFXHMIlZx7CDjwMXdYfyGn6IvIaja8Ri8MwXPtBXQ/XLiGw8tRpvH/wMM4WFKBLRDgmtmuDzuHh5d7+ci9Kk82G75JTsORECi5YLIg1GHBnXFPc1rQxQtnjccWqW+CoK6q63YUQOJmXjz1Fwzv2mLKQZDZ7TuKLCwr0hOhO4WFoFlyze6W9wde6PMprdyEEsux2nCsoKArdBThbUIBzJQJ4vtNZ6j56pbIwdAeU3/MdrtUyfJdw1a93ISC58soJ4KlFveH/Xla4yi7kJyQtXJrIy/aGu9VhpXqkFfZMGPfdA3XufmS3fAeW+iOv/DH4GcO1H9TVcC2EwPrzF/C/A4dwNCcHrUNDMKFtG/SpF3XJNz5vX5ROtxu/nzuPxcdPYHtGJnRKJRIbNsDo+Di0DLnM4Ekqg4FDHr5u93yHA3uzsrHHZPIM88guGv8bqFKhgzEMHcMLw3QHY1id/IOUr3V5XEm7CyGQ43CUCNz/hu7iQJ7jKD2sRqtQ/NvzXSJ0FwfySJ2uTp0g78/Xu+TML+rxTvME8JKXC8N4GhROc5n7CkldGMI19eDS1oM67xCU9lSY2nwEW8T1fqnfV6pjuOYJjbXAtvQMzNx/ELtNJjQODMCMbl1wQ4NYn76hqRQK3NAgFjc0iMXhbDO+On4CK06fwfcpJ9ElIhyjm8VhUEx9qBTVe2wW0ZUSQiAlL8/TI73HZMJRc06JXukgDIyJRiejER3DjYgLCqr1vdJUu0iShBCNBiEaDdqEhpZ7mzyH49/QXaL3+3xBAQ6dy/YMeSqmVigQrdeXGG7y7/CT+gYD6ul1/Ny4QkIVAJcqDi5D3KVv6LJAWRy0bReKAngalLYLUNjToCpIhlBokdHxGzhCuHaJL7DnugY7mJWNWQcOYlNqGqJ0Ooxr3Qq3NGkEdSXeqK7mL75sux0/JJ/E1ydO4FyBBdF6PUbGNcF/mjaBUau9on3WFezNk0dl2j3P4fCMkd6dmYW9WSbPyXBBahU6hBnRMTwMnYp6pXliWPn4WpeHXO1e4HTifIHl3yEnFw07SbeWPqlPKUmI1usLe7wDCoP3bU0ao76hZk5lx9e7/7HnmnyieAGY1WfOIlitxjPt2mJ0fBx0fp5KKlSjwYMtm2NMi3hsOH8Bi4+dwP8OHMJHh47gxoYNcHezOLQJC/VrTeRbNpcLOQ4HcuwO5DjsyLE7oJAkzzRvuuIp4ZTVZ7q3K+EWAsm5eZ7hHbtNWTiWkwOBwvmhmwUHYXBMDDoWDfOICwqqU191E3nLoFKhWXAQmgUHlXu9zeXyhO+SAfxsvgV/p2UgzWLBqtNn8c2AvvyDlWoshusa5OIFYB5t1QL3N4+X/Q1IKUkYFFMfg2Lq41hODr46noyfTp7CspOn0DnciNHN4jA4NqZSPerkG0IIFDidMBcF5FyHA2a73ROYcx0OmItCc67DgRyHA+biy3Y7bO7KrNNXGET/Ddylw7fOM/dyYRAvfV2JOZuVSuiK5m3WFc/Z7Lms9MlXyLlFvdKFQzwKZ/EoHksarFajgzEMNzSIQSejEe2NYZzvnchHtEolmgQFVrgi8I6MTNz/xyY8+892fNS7J4dWUY3EYSE1wMULwNxRtABMpE531fuuqq9Tcux2LDt5CouPJ+N0fj4idTqMjGuCO5o2QYQP6q7pKtPuLiGQay8MwblFodhcFH5L/lwciP/taS7cdqn5ciUAQWo1gtVqBGvUCFKrEaLRIFhdeDlYo0aIWoMgTdFt1GoIwLNCoLXEMtpWz1LaJa4r2mZ1lV5qu+RtK0slSTCUCOe6osVTdMrSl4sDuaEo5CsVEk7ZbNh67jyO5+R6eqWbBwd7eqQ7GY1oEhTIXmkf4tfk8qjJ7f59cgom79yNMc3j8XyHdnKXUyk1ud1rKg4LoUq53AIw1VmwRoN7m8fj7vhm+PNCKhYfP4EPDh7G3ENHMKRBLEbHx6Gj0Sh3mX5X4HQiJS8PtvwCnMnMLDXcIqeo59gTkItC8sVTY11MVXQSUrBajSCNGqEaDRoHBpYKzSFF1wWrNQguEZQD1WpZg6QQAja32xPQC4P4xYG99HUFLiesxbe76LYmmw3Wi4K+o0Tve5hWi/ZhoRjaINYzg0cge6WJqpXbmzbBYbMZC48eQ8uQYNzcuJHcJRFVCsN1NeTtAjA1gUKS0K9+NPrVj0ZKbh6+On4CS0+eworTZ9A+LBSjm8VhSINYaKrZ0sNXQwiBCxYLknPzCv/l5XouX7CUXRwAKJxLNkSj8fQWxxgMaF3Uk3xxIA4uCtLF23RKZY0c5wwUzk5Q3OMcVkXHcLjdsLlcsLncaBEbg8zMzCo6EhH5ygsd2uN4Ti4m79yNJoGB6Bhe9zpjqObisJBqpLwFYJ5u1xadqvBNRZYV6xwO/HTqNL46fgIncvMQrtXijqZNMDKuCaL0er/WcjWKe6FTikN0bi6Si34uOdwhQKVCXNEYw6aBQWgaFIhW9etDFBQgWFPYe6zheHS/4Fe2/sc2l0dtaPdsmx0j12+AxeXCdwP7o14N+HyoDe1e01THYSEM19XAlS4A4wtyviiFENiSlo7Fx09gw/kLUEoSBsfGYHSzOHQON1aL3tjyeqFTcvNw4qJeaAlArMGAJkGBiAsKKgrShZcjdGVXMOMbsDzY7v7HNpdHbWn3ozk5uGv9H4gLCsQX/a7z+6xYlVVb2r0mqY7hmsNCZOaPBWCqK0mS0KteFHrVi8LpvHx8deIElqacwuozZ9E6NASjm8VhWMMG0PrhzdRS1Aud7GUvdNfIcE8vdNOgQDQKDKz2b/pERDVN8+BgTO96LZ7YshX/3bEL07teWy06XoguheFaJhcvAPNK506VXgCmNmkYGIAXOrTHE21aY/mp01h8/ARe3rEL7+47gNubNsadcU0Rc5WLCpTshU7Jy8OJ3Mv3QneNiLhsLzQREVWdgTH18WTb1vjfgUNoFRKCB1s2l7skoktiuPaz6rIATHVlUKkwMq4p7mjaBP+kZ2Dx8RP47MhRfHbkKAbF1Mfo+Dh0jYi4ZMC9uBe6ZJBmLzQRUc3zSMsWSDLn4L39BxAfHIR+9aPlLomoQgzXflJdF4CpriRJQveoSHSPisTZ/AIsOZGM71JS8Nu582gRHIxRzeLQq14kTuXllwrPyXl5OF/AXmgiotpEkiRMvbYzUvLy8Ow/2/HNgH4VrgJJJDee0FjFqnIBGF+oSSdfWF0urDx9BouPncBhs7nUdeXNyFGde6FrUrvXJmx3/2Oby6O2tvv5ggLcsW4jAlQqLBnYDyHVrIOqtrZ7dcYTGuuQmrwATHWlUypxW5PGuLVxI+zMNOF4Ti4aBwawF5qIqI6obzBgds9uuG/jJjyzdRvm9u4JVR09V4mqL4ZrH6tNC8BUV5Ik4dqIcFwbES53KURE5Gedw8Mx5ZpO+G/RSe+TOraXuySiUhiufaS8BWDe79m9SheAISIiqotua9IYSWYzvjh2HC1CgnFrk8Zyl0TkwXB9lcpbAGZy555+WQCGiIiornqufTscy8nFq7v2oGlQIDqH89tMqh44UOkqbTh/AY9v2Qq724UZ3brgu4H9cV10PQZrIiKiKqRSKDCje1dE6/V4css/OF9QIHdJRAAYrq9a3/rRmN71Wvw8eBCGNmxQJ1ZWJCIiqg5CNRrM6dUdVpcLT2zZCovTKXdJRAzXV0spSUhs1LDOrqxIREQkp/jgYLzTrQsOZZvx3x27UMtmGKYaiImQiIiIarT+9aMxoV0brDpzFp8cOSp3OVTH8YRGIiIiqvEeatEcSeYc/O/AQcQHB2FgTH25S6I6ij3XREREVONJkoTXr+2MNqGheH7bDhzNyZG7JKqjGK6JiIioVtAplXi/Z3folUo8vvlvZNvscpdEdRDDNREREdUa0QY93u/ZHRcsVkzc+g8cbrfcJVEdw3BNREREtUqncCNevaYTtqZnYPre/XKXQ3UMT2gkIiKiWmdE40Y4Yjbj86PH0TIkGLc3bSJ3SVRHsOeaiIiIaqVn2rVF76govLZrD3ZkZMpdDtURDNdERERUK6kUCrzbvQtiAwx46u+tOMcl0skPGK6JiIio1grRaDCnZw/YXW48vnkrCrhEOlUxhmsiIiKq1eKCg/BOty44Yjbj/7bv5BLpVKUYromIiKjW61c/Gk+3a4s1Z89h3uEkucuhWozhmoiIiOqEB1rEY3ijBph98BDWnj0ndzl0lc4VFGBJ0lG5yyjDb1Pxffjhh9i5cydCQkIwY8YMAEBeXh5mzpyJ9PR0REZGYuLEiQgMDIQQAgsWLMCuXbug1Woxbtw4xMXF+atUIiIiqoUkScKr13RGSm4eJm3fia8CA9AiJETusqiSksxmfJZ0DKtOn4FSocD6oTcgVKuRuywPv/Vc9+/fHy+99FKpbcuWLUP79u0xe/ZstG/fHsuWLQMA7Nq1CxcuXMDs2bPxyCOPYP78+f4qk4iIiGoxnVKJ2T27I0ClwuObtyLLZpO7JPKCEALb0jMw9q8tGLF2PX47ew6jm8Vhy8j/VKtgDfgxXLdp0waBgYGltm3btg39+vUDAPTr1w/btm0DAGzfvh19+/aFJElo0aIF8vPzkZWV5a9SiYiIqBarp9djds9uSLNaMXHrNi6RXo25hMCvZ8/izvV/4L4/NmF/VhaebNsav994A17o2B4NggIvvxM/k3WFRrPZjLCwMABAaGgozGYzAMBkMiEiIsJzu/DwcJhMJs9tiYiIiK5GR6MRr13TCZO278S0Pfvw384d5S6JSrC5XPjp5GksOHoUJ/Py0TAgAJM7d8SIxo2gUyrlLu+Sqs3y55IkQZKkSt9v7dq1WLt2LQBg2rRppUI5XZ5KpWKbyYDtLg+2u/+xzeXBdvfOAxEROGV34MO9+3BNbAzua9P6qvbHdr962TYbFh44hE/2H0C6xYJOkRGY3LMHbmzSGEpF2QEX1bHNZQ3XISEhyMrKQlhYGLKyshAcHAwAMBqNyMjI8NwuMzMTRqOx3H0kJCQgISHB83PJ+9HlRUREsM1kwHaXB9vd/9jm8mC7e++x+DjsTU3Fi5s2o54koUvklQc1tvuVO19QgC+OHcd3ySdR4HSiT70ovNP1GnSNiIAkScgymcq9n5xtHhMTU+52Wafi69KlCzZu3AgA2LhxI7p27erZ/scff0AIgaSkJBgMBg4JISIiIp9TShLe7dYFDQMC8NTf/+Bsfr7cJdUpR805eHHbDtzwy29YdOwEBsVEY2nCAHzcpxe6RUZe0agGufmt53rWrFk4ePAgcnNzMXbsWNxxxx0YMWIEZs6ciXXr1nmm4gOAzp07Y+fOnXjyySeh0Wgwbtw4f5VJREREdUywRoM5vXpg5PoNeHzLVizq3xcBqmozcrbWEUJgR0YmPk06io0XUqFXKnFXs6a4Nz4esQEGucu7apKoZWuAnjvHSeErg19hyYPtLg+2u/+xzeXBdr8ymy6kYuxfW5AQG4P3uneFopK9pmz3S3MJgXXnzuPTpKPYa8qCUavB3c2a4c64plc8nV51HBbCP8uIiIiIAPSJrodn27fD9H378dGhIxjfppXcJdUKNpcLP586jQVJx5CSl4eGAQZM7tQRI5pU/5k/rgTDNREREVGR+5o3wxGzGXMOHUaLkGAMji2/d5IuL8dux5LkFHx57DgyrDa0DQ3Fe927YnBsDJQ1cCy1txiuiYiIiIpIkoRXrumE5Lw8TNq2Aw0DAtAqlEukV8aFAgu+OHYc3yaneGb+eKBrc3SPjKiRJyhWFsM1ERERUQlapRLv9+iO/6zbgMe3/I1vB/aHUauVu6xq72hODhYkHcWKU2cgAAxpEIsHWzSvc3+cMFwTERERXSRSr8P7Pbvj3o1/YsLf/2D+db2hKWcRk7pOCIEdmZn47MhRbCia+ePOuKa4r3kzxAYEyF2eLBiuiYiIiMrR3hiG16/tjOe37cBbu/diyjWd5C6p2nAXzfzxWdIx7DaZEKbR4PE2rTAqLu6KZ/6oLRiuiYiIiCqQ2Kghksw5mJ90FC1DQnBns6ZylyQre4mZP5Lz8tDAYMB/O3XAiMaNoOfc4AAYromIiIgu6al2bXA0Jwdv7tmLuOBAdIuMlLskv8ux2/Ftcgq+KJr5o01oCGZ064LBsTFQcbhMKQzXRERERJeglCRM79YFd63fiAl/b8O3A/uhQR0ZT5xqseCLo4Uzf+Q7negdFYW3uzZHjzoy88eVYLgmIiIiuowgtRof9OyBO9dvxPjNW/FV/+sQoFbLXVaVOZaTgwVJx7D81Gm4hcDQhg1wf4t4tAkNlbu0ao/hmoiIiMgLTYIC8V73rnhk02ZM2r4T/+vRrdJLpFd3OzMy8WnSUaw/fwE6pRJ3xDXBmObxdaan3hcYromIiIi81KteFJ7v0A7T9u7HnIOH8UTb1nKXdNXcQmD9+Qv4LOkodmWaEKrRYHzrVhjVrCnCOL93pTFcExEREVXCPfHNcMScg48OH0HzkGAMaRArd0nlcrrdyHE4YLbbkW23I9tW+L/Z7ij82W6H2W7HEXMOUvLyEGsw4OVOHXALZ/64Kmw5IiIiokqQJAlTOndEcm4eXtq+E40CA6p0LLIQAnlOJ7Jtdpgd5Yfk4qBstv97XY7DUeE+lZKEEI0aoRoNovV6PN6mFa7nzB8+wXBNREREVEkapRL/69kNI9dtxOObt+K7gf0R4cX9rC5XmZBcXm9yyZBsttvhFKLCfQapVQjVaBCq0SBEo0GjwMCiy2rP9uLrQjUahGo1CFSpONtHFWG4JiIiIroCkbrCJdLv3vAHnvp7K+5r1w5nTJnILgrKJUNycVC2ulwV7k+nVJYKxfHBwSVCsbp0QC4KycFqNXubqxmGayIiIqIr1DYsFG90uQbP/rMdOzf+AaD0kItQjQYxBgNah4aW6EFWlwrIIerC/3VKpcyPhnyB4ZqIiIjoKtzYsAGuCQ9HYGgIRH4+h1zUcQzXRERERFcp2qBHRHAwMux2uUshmXGQDhERERGRjzBcExERERH5CMM1EREREZGPMFwTEREREfkIwzURERERkY8wXBMRERER+QjDNRERERGRjzBcExERERH5CMM1EREREZGPMFwTEREREfmIJIQQchdBRERERFQbsOe6jps0aZLcJdRJbHd5sN39j20uD7a7PNju/lcd25zhmoiIiIjIRxiuiYiIiIh8hOG6jktISJC7hDqJ7S4Ptrv/sc3lwXaXB9vd/6pjm/OERiIiIiIiH2HPNRERERGRj6jkLoB8KyMjA3PmzEF2djYkSUJCQgJuvPFG5OXlYebMmUhPT0dkZCQmTpyIwMBAnD17Fh9++CGSk5Nx55134qabbvLsa/fu3ViwYAHcbjcGDRqEESNGyPfAqjlftXtF+6Hy+fL1DgButxuTJk2C0WislmegVwe+bPP8/HzMnTsXp0+fhiRJeOyxx9CiRQsZH1315ct2X7FiBdatWwdJktCwYUOMGzcOGo1GxkdXfVW23f/880/89NNPEEJAr9fjoYceQpMmTQDwM9VbvmpzWT9PBdUqJpNJHD9+XAghREFBgXjyySfF6dOnxZdffimWLl0qhBBi6dKl4ssvvxRCCJGdnS2OHj0qvvrqK/HTTz959uNyucTjjz8uLly4IBwOh3j22WfF6dOn/f54agpftXtF+6Hy+ardiy1fvlzMmjVLvPXWW357DDWNL9v8/fffF2vXrhVCCOFwOEReXp7/HkgN46t2z8zMFOPGjRM2m00IIcSMGTPE+vXr/fpYapLKtvvhw4dFbm6uEEKInTt3ihdffFEIwc/UyvBVm8v5ecphIbVMWFgY4uLiAAB6vR6xsbEwmUzYtm0b+vXrBwDo168ftm3bBgAICQlBfHw8lEplqf0cO3YM0dHRqFevHlQqFXr16uW5D5Xlq3avaD9UPl+1OwBkZmZi586dGDRokP8eQA3kqzYvKCjAoUOHMHDgQACASqVCQECAHx9JzeLL17rb7YbdbofL5YLdbkdYWJj/HkgNU9l2b9myJQIDAwEAzZs3R2ZmJgB+plaGr9pczs9TDgupxdLS0pCcnIz4+HiYzWbPG2hoaCjMZvMl72symRAeHu75OTw8HEePHq3SemuLq2n3ivZDl3e17b5w4ULcfffdsFgsVV1qrXE1bZ6Wlobg4GB8+OGHOHnyJOLi4jBmzBjodDp/lF6jXU27G41GDB8+HI899hg0Gg06duyIjh07+qPsGq+y7b5u3Tp07twZAD9Tr9TVtHlF+/EH9lzXUlarFTNmzMCYMWNgMBhKXSdJEiRJkqmy2s1X7X6p/VBZV9vuO3bsQEhIiKeXgy7vatvc5XIhOTkZ119/PaZPnw6tVotly5ZVYcW1w9W2e15eHrZt24Y5c+Zg3rx5sFqt+OOPP6qy5Fqhsu2+f/9+rF+/HqNHj/ZnmbWKr9pcjs9ThutayOl0YsaMGbjuuuvQvXt3AIVfEWZlZQEAsrKyEBwcfMl9GI1Gz1crQOFX5kajseqKrgV80e4V7Ycq5ot2P3LkCLZv347x48dj1qxZ2L9/P2bPnl3ltddUvmjz8PBwhIeHo3nz5gCAHj16IDk5uWoLr+F80e779u1DVFQUgoODoVKp0L17dyQlJVV57TVZZdv95MmTmDdvHp577jkEBQUB4GdqZfmizSvajz8wXNcyQgjMnTsXsbGxSExM9Gzv0qULNm7cCADYuHEjunbtesn9NGvWDOfPn0daWhqcTic2b96MLl26VGntNZmv2r2i/VD5fNXuo0aNwty5czFnzhxMmDAB7dq1w5NPPlmltddUvmrz0NBQhIeH49y5cwAKQ1+DBg2qrvAazlftHhERgaNHj8Jms0EIgX379iE2NrZKa6/JKtvuGRkZePfdd/H4448jJibGc3t+pnrPV20u5+cpF5GpZQ4fPozJkyejUaNGnq9M7rrrLjRv3hwzZ85ERkZGqSlssrOzMWnSJFgsFkiSBJ1Oh/feew8GgwE7d+7E559/DrfbjQEDBuDWW2+V+dFVX75q91OnTpW7n2uuuUbOh1dt+fL1XuzAgQNYvnw5p+KrgC/bPCUlBXPnzoXT6URUVBTGjRvnOTGJSvNlu3/77bfYvHkzlEolmjRpgrFjx0KtVsv8CKunyrb73LlzsXXrVkRERAAAlEolpk2bBgD8TPWSr9q8ov344/OU4ZqIiIiIyEc4LISIiIiIyEcYromIiIiIfIThmoiIiIjIRxiuiYiIiIh8hOGaiIiIiMhHGK6JiIiIiHyE4ZqIiIiIyEcYromIqFJcLpfcJRARVVsquQsgIiLf+fnnn5GUlIRnn33Ws+2zzz6DJEkYOXIkPv/8c+zatQuSJGHAgAG44447oFAocOHCBcybNw8nT56EJEno2LEjHnzwQQQEBAAAxo8fj8GDB2PTpk04d+4cvvzySyiVSrkeJhFRtcWeayKiWuS6667Dnj17kJ+fD6Cwl3nz5s3o168f5syZA6VSidmzZ2P69OnYs2cPfv/9d899b7nlFsybNw8zZ85EZmYmvvvuu1L7/uuvvzBp0iQsXLiQwZqIqAIM10REtUhYWBhat26NLVu2AAB2796NoKAgGI1G7Nq1C2PGjIFOp0NISAiGDRuGzZs3AwCio6PRoUMHqNVqBAcHY9iwYTh48GCpfQ8dOhQRERHQaDR+f1xERDUFh4UQEdUy/fr1w6+//oqEhAT8+eef6Nu3LzIyMuByufDII494bieEQHh4OAAgOzsbCxcuxKFDh2C1WuF2uxEYGFhqvxEREX59HERENRHDNRFRLdO1a1fMnz8fp06dwo4dO3D33XdDqVRCpVLh008/LXdIx9dffw0AmDFjBgIDA/HPP//gs88+83fpREQ1HoeFEBHVMhqNBt27d8fs2bMRHx+PiIgIhIWFoWPHjvjiiy9QUFAAt9uNCxcueIZ+WCwW6HQ6GAwGmEwmLF++XOZHQURUMzFcExHVQv3798epU6fQt29fz7bHH38cTqcTTz/9NO6//3689957yMrKAgD85z//QXJyMu677z689dZb6Natm1ylExHVaJIQQshdBBER+VZGRgYmTJiAjz/+GAaDQe5yiIjqDPZcExHVMm63GytWrECvXr0YrImI/IzhmoioFrFarbjvvvuwd+9e3HHHHXKXQ0RU53BYCBERERGRj7DnmoiIiIjIRxiuiYiIiIh8hOGaiIiIiMhHGK6JiIiIiHyE4ZqIiIiIyEcYromIiIiIfOT/Ac8TCqbeU/ivAAAAAElFTkSuQmCC"
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    <span class="icon"><i class="fab fa-python"></i></span>
    <span class=>Python</span>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#examine pre Covid and post 2019 foot traffic</span>
<span class="n">ds</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">sensor_ds</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s2">&quot;dow&quot;</span><span class="p">,</span><span class="s2">&quot;day&quot;</span><span class="p">])[</span><span class="s2">&quot;pre2020_hourly_counts&quot;</span><span class="p">,</span><span class="s2">&quot;post2019_hourly_counts&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;dow&#39;</span><span class="p">])</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;#0f9295&quot;</span><span class="p">,</span><span class="s2">&quot;orange&quot;</span><span class="p">])</span>
<span class="n">axs</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Foot Traffic by Day of Week - All Hours&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">axs</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Average hourly counts&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
     </code></pre></div>
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"
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<p>The top 20 locations by foot traffic overall are displayed below.</p>

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<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div>
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    <span class="icon"><i class="fab fa-python"></i></span>
    <span class=>Python</span>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#distribution by traffic, by day</span>
<span class="n">pivot</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">sensor_ds</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s1">&#39;day_counts&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span><span class="s1">&#39;sensor_description&#39;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
<span class="n">pivot_ds</span> <span class="o">=</span> <span class="n">pivot</span><span class="p">[</span><span class="s1">&#39;day_counts&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">pivot_ds</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;orange&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>

<span class="c1">#by night</span>
<span class="n">pivot</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">sensor_ds</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s1">&#39;night_counts&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span><span class="s1">&#39;sensor_description&#39;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
<span class="n">pivot_ds</span> <span class="o">=</span> <span class="n">pivot</span><span class="p">[</span><span class="s1">&#39;night_counts&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">pivot_ds</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;#056b8a&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>

<span class="n">axs</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Top 20: Foot Traffic by location by day and night&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">axs</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Average counts&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
     </code></pre></div>
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"
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<p>We will investigate if the traffic volumes by location are different depending on day and night traffic.</p>

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<div class="usecase-section-header">Day Economy</div>

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<h2 id="Compare-day-traffic-before-Covid-and-now">Compare day traffic before Covid and now<a class="anchor-link" href="#Compare-day-traffic-before-Covid-and-now">&#182;</a></h2>
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    <span class="icon"><i class="fab fa-python"></i></span>
    <span class=>Python</span>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#distribution by traffic, by day</span>
<span class="n">pivot</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df_day</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s1">&#39;pre2020_hourly_counts&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span><span class="s1">&#39;sensor_description&#39;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
<span class="n">pivot_ds</span> <span class="o">=</span> <span class="n">pivot</span><span class="p">[</span><span class="s1">&#39;pre2020_hourly_counts&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">pivot_ds</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;#0f9295&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">rot</span><span class="o">=</span><span class="mi">90</span><span class="p">);</span>

<span class="c1">#by night</span>
<span class="n">pivot</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df_day</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s1">&#39;post2019_hourly_counts&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span><span class="s1">&#39;sensor_description&#39;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
<span class="n">pivot_ds</span> <span class="o">=</span> <span class="n">pivot</span><span class="p">[</span><span class="s1">&#39;post2019_hourly_counts&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">pivot_ds</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;orange&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">rot</span><span class="o">=</span><span class="mi">90</span><span class="p">);</span>

<span class="n">axs</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Top 20: Foot Traffic by location by day&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">axs</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Average counts&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
     </code></pre></div>
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"
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<p>The chart above shows how signicantly traffic has changed after Covid from 2020 onwards for the top 20 locations.</p>
<p>We can see changes in the traffic of each sensor before Covid and after Covid on the map below. Please right click on the icon in the top right corner of the map and select the layer to see traffic before Covid.</p>

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<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div>
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  <div class="snippet-language">
    <span class="icon"><i class="fab fa-python"></i></span>
    <span class=>Python</span>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#Visualise day data</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">folium</span><span class="o">.</span><span class="n">Map</span><span class="p">(</span><span class="n">location</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mf">37.8167</span><span class="p">,</span> <span class="mf">144.967</span><span class="p">],</span> <span class="n">zoom_start</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span><span class="c1"># tiles=&#39;Stamen Toner&#39;</span>
<span class="n">locations</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_daypft_beforecovid_avg</span><span class="p">)):</span>
    <span class="n">row</span> <span class="o">=</span><span class="n">df_daypft_beforecovid_avg</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="n">location</span> <span class="o">=</span> <span class="p">[(</span><span class="n">row</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span><span class="n">row</span><span class="o">.</span><span class="n">lon</span><span class="p">)]</span><span class="o">*</span><span class="nb">int</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;hourly_counts&#39;</span><span class="p">])</span>
    <span class="n">locations</span> <span class="o">+=</span> <span class="n">location</span>
<span class="n">marker_cluster</span>  <span class="o">=</span> <span class="n">MarkerCluster</span><span class="p">(</span>
  <span class="n">name</span><span class="o">=</span><span class="s1">&#39;day traffic before covid-19&#39;</span><span class="p">,</span>
  <span class="n">locations</span><span class="o">=</span><span class="n">locations</span><span class="p">,</span>
  <span class="n">overlay</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
  <span class="n">control</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
  <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span>
  <span class="n">show</span><span class="o">=</span><span class="kc">False</span> <span class="c1"># this removes from automatic selection in display - need to select to show data points</span>
  <span class="p">)</span>
<span class="n">marker_cluster</span><span class="o">.</span><span class="n">add_to</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>

<span class="c1">#next layer</span>
<span class="n">locations</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_daypft_aftercovid_avg</span><span class="p">)):</span>
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    <span class="n">location</span> <span class="o">=</span> <span class="p">[(</span><span class="n">row</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span><span class="n">row</span><span class="o">.</span><span class="n">lon</span><span class="p">)]</span><span class="o">*</span><span class="nb">int</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;hourly_counts&#39;</span><span class="p">])</span>
    <span class="n">locations</span> <span class="o">+=</span> <span class="n">location</span>
<span class="n">marker_cluster</span>  <span class="o">=</span> <span class="n">MarkerCluster</span><span class="p">(</span>
  <span class="n">name</span><span class="o">=</span><span class="s1">&#39;day traffic after covid-19&#39;</span><span class="p">,</span>
  <span class="n">locations</span><span class="o">=</span><span class="n">locations</span><span class="p">,</span>
  <span class="n">overlay</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
  <span class="n">control</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
  <span class="p">)</span> 
<span class="n">marker_cluster</span><span class="o">.</span><span class="n">add_to</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">folium</span><span class="o">.</span><span class="n">LayerControl</span><span class="p">()</span><span class="o">.</span><span class="n">add_to</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">m</span>
</code></pre></div>
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<h2 id="Clustering:-Day-Traffic">Clustering: Day Traffic<a class="anchor-link" href="#Clustering:-Day-Traffic">&#182;</a></h2><p>Discover if any patterns in day traffic, the day hours used were from 5am to 5pm.</p>
<p>Similarities between locations in terms of traffic, may mean similar solutions may be used to improve pedestrian traffic.</p>

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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#identify class and features</span>
<span class="n">use_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;sensor_id&quot;</span><span class="p">,</span><span class="s2">&quot;lat&quot;</span><span class="p">,</span><span class="s2">&quot;lon&quot;</span><span class="p">,</span><span class="s2">&quot;hourly_counts&quot;</span><span class="p">]</span>
<span class="n">loc_day</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">df_day</span><span class="p">[</span><span class="n">use_list</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">loc_day</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>

<span class="n">codes</span> <span class="o">=</span> <span class="n">loc_day</span><span class="p">[[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">]]</span> <span class="c1">#class</span>
<span class="n">loc_day</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="n">loc_day</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<pre><code class="language-python" data-lang="python">    sensor_id       lat         lon  hourly_counts
0          34 -37.81538  144.974151            300
12         34 -37.81538  144.974151              7
13         34 -37.81538  144.974151             16
14         34 -37.81538  144.974151             37
15         34 -37.81538  144.974151             36
</code></pre>
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      <th></th>
      <th>lat</th>
      <th>lon</th>
      <th>hourly_counts</th>
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      <th>0</th>
      <td>-37.81538</td>
      <td>144.974151</td>
      <td>300</td>
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    <tr>
      <th>12</th>
      <td>-37.81538</td>
      <td>144.974151</td>
      <td>7</td>
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      <th>13</th>
      <td>-37.81538</td>
      <td>144.974151</td>
      <td>16</td>
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      <th>14</th>
      <td>-37.81538</td>
      <td>144.974151</td>
      <td>37</td>
    </tr>
    <tr>
      <th>15</th>
      <td>-37.81538</td>
      <td>144.974151</td>
      <td>36</td>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#examine data</span>
<span class="n">loc_day</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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      <th></th>
      <th>lat</th>
      <th>lon</th>
      <th>hourly_counts</th>
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      <th>count</th>
      <td>2.455152e+06</td>
      <td>2.455152e+06</td>
      <td>2.455152e+06</td>
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    <tr>
      <th>mean</th>
      <td>-3.781342e+01</td>
      <td>1.449621e+02</td>
      <td>6.643425e+02</td>
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      <th>std</th>
      <td>6.207406e-03</td>
      <td>8.544593e-03</td>
      <td>8.556853e+02</td>
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      <th>min</th>
      <td>-3.782402e+01</td>
      <td>1.449297e+02</td>
      <td>1.000000e+00</td>
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    <tr>
      <th>25%</th>
      <td>-3.781874e+01</td>
      <td>1.449587e+02</td>
      <td>1.050000e+02</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>-3.781381e+01</td>
      <td>1.449651e+02</td>
      <td>3.020000e+02</td>
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    <tr>
      <th>75%</th>
      <td>-3.781102e+01</td>
      <td>1.449669e+02</td>
      <td>8.760000e+02</td>
    </tr>
    <tr>
      <th>max</th>
      <td>-3.779432e+01</td>
      <td>1.449747e+02</td>
      <td>1.161200e+04</td>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1"># identify optimal clusters </span>
<span class="n">features_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;lat&quot;</span><span class="p">,</span><span class="s2">&quot;lon&quot;</span><span class="p">,</span><span class="s2">&quot;hourly_counts&quot;</span><span class="p">]</span>
<span class="n">df_features</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">loc_day</span><span class="p">[</span><span class="n">features_list</span><span class="p">])</span>

<span class="c1">#scale the features</span>
<span class="n">mms</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
<span class="n">mms</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df_features</span><span class="p">)</span> <span class="c1">#day hours</span>
<span class="n">data_transformed</span> <span class="o">=</span> <span class="n">mms</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_features</span><span class="p">)</span>

<span class="c1">#data_transformed</span>

<span class="c1"># for each k value calculate sum of squared distances to the nearest cluster centre</span>
<span class="n">Sum_of_squared_distances</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">K</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">)</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">K</span><span class="p">:</span>
    <span class="n">km</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">k</span><span class="p">)</span>
    <span class="n">km</span> <span class="o">=</span> <span class="n">km</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data_transformed</span><span class="p">)</span>
    <span class="n">Sum_of_squared_distances</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">km</span><span class="o">.</span><span class="n">inertia_</span><span class="p">)</span>
    
<span class="c1">#plot results</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">K</span><span class="p">,</span> <span class="n">Sum_of_squared_distances</span><span class="p">,</span> <span class="s1">&#39;g*-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Number of Clusters&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Sum of squared distances&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Elbow Method&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>For day traffic the optimal number of clusters is 3.</p>

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<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div>
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    <span class="icon"><i class="fab fa-python"></i></span>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1"># run k-means clustering, define centroids</span>
<span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">loc_day</span><span class="p">)</span>
<span class="n">centroids</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">cluster_centers_</span>

<span class="n">codes</span><span class="p">[</span><span class="s1">&#39;cluster&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span>
<span class="c1">#codes.head()</span>
<code class="language-python" data-lang="python"></pre></div>
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    <span class="icon"><i class="fab fa-python"></i></span>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">df_features</span>

<span class="c1">#visualise data</span>
<span class="n">x_axis</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;lon&#39;</span><span class="p">]</span>
<span class="n">y_axis</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">]</span>
<span class="n">cluster_legend</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">scatterplot</span><span class="p">(</span><span class="n">x_axis</span><span class="p">,</span> <span class="n">y_axis</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="n">cluster_legend</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">cluster_legend</span><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s1">&#39;colorblind&#39;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">60</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Clustering Pedestrian Counts: Day Hours&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
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<h5 id="The-exploratory-data-analysis-on-day-and-night-showing-initial-findings-is-available,-please-refer-to-eda-compare-pedestrian-traffic-day-night">The exploratory data analysis on day and night showing initial findings is available, please refer to <a href="https://github.com/Chameleon-company/MOP-Code/blob/barkhaj-branch/datascience/dataanalysis/Other%20EDA/eda_compare_pedestrian_traffic_day_night.ipynb">eda compare pedestrian traffic day night</a><a class="anchor-link" href="#The-exploratory-data-analysis-on-day-and-night-showing-initial-findings-is-available,-please-refer-to-eda-compare-pedestrian-traffic-day-night">&#182;</a></h5>
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<p>There are three clusters in the day traffic. They indicate different levels of traffic common among sensors located in the City of Melbourne. We can use these findings to investigate what makes the traffic activity for these locations similar.</p>
<p>Similar sensor locations in terms of pedestrian counts, may have similar solutions that can be applied to increase activity.</p>
<p>Next steps for this component, we can add in additional factors impacting traffic, and experiment with additional types of clustering techniques, to improve cluster detection and investigate the impact of these factors.</p>
<p>The factors to consider are many and are discussed in the use case on <a href="https://github.com/Chameleon-company/MOP-Code/blob/barkhaj-branch/datascience/dataanalysis/Other%20EDA/evaluate_factors_impacting_pedestrian_traffic.ipynb">factors impacting pedestrian traffic</a></p>

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<div class="usecase-section-header">Night Economy</div>

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<h2 id="Compare-night-traffic-before-Covid-and-now">Compare night traffic before Covid and now<a class="anchor-link" href="#Compare-night-traffic-before-Covid-and-now">&#182;</a></h2>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#distribution by traffic</span>
<span class="c1">#by night before covid</span>
<span class="n">pivot</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df_night_beforecovid</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s1">&#39;pre2020_hourly_counts&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span><span class="s1">&#39;sensor_description&#39;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
<span class="n">pivot_ds</span> <span class="o">=</span> <span class="n">pivot</span><span class="p">[</span><span class="s1">&#39;pre2020_hourly_counts&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">pivot_ds</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;orange&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>

<span class="c1">#by night after Covid</span>
<span class="n">pivot</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df_night_aftercovid</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s1">&#39;post2019_hourly_counts&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span><span class="s1">&#39;sensor_description&#39;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
<span class="n">pivot_ds</span> <span class="o">=</span> <span class="n">pivot</span><span class="p">[</span><span class="s1">&#39;post2019_hourly_counts&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">pivot_ds</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;#056b8a&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>

<span class="n">axs</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Top 20: Foot Traffic by location by night, before and after Covid&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">axs</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Average counts&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
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"
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<p>The top 20 locations for night traffic, are different to the day traffic locations.</p>
<p>View the different layers showing before Covid and after Covid hourly traffic for night locations on the map below.</p>

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<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div>
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    <span class="icon"><i class="fab fa-python"></i></span>
    <span class=>Python</span>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#Visualise night data</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">folium</span><span class="o">.</span><span class="n">Map</span><span class="p">(</span><span class="n">location</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mf">37.8167</span><span class="p">,</span> <span class="mf">144.967</span><span class="p">],</span> <span class="n">zoom_start</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
<span class="n">locations</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_night_beforecovid_avg</span><span class="p">)):</span>
    <span class="n">row</span> <span class="o">=</span><span class="n">df_night_beforecovid_avg</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="n">location</span> <span class="o">=</span> <span class="p">[(</span><span class="n">row</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span><span class="n">row</span><span class="o">.</span><span class="n">lon</span><span class="p">)]</span><span class="o">*</span><span class="nb">int</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;pre2020_hourly_counts&#39;</span><span class="p">])</span>
    <span class="n">locations</span> <span class="o">+=</span> <span class="n">location</span>
<span class="n">marker_cluster</span>  <span class="o">=</span> <span class="n">MarkerCluster</span><span class="p">(</span>
  <span class="n">name</span><span class="o">=</span><span class="s1">&#39;Night traffic before Covid-19&#39;</span><span class="p">,</span>
  <span class="n">locations</span><span class="o">=</span><span class="n">locations</span><span class="p">,</span>
  <span class="n">overlay</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
  <span class="n">control</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
  <span class="n">show</span><span class="o">=</span><span class="kc">False</span> <span class="c1"># this removes from automatic selection in display - need to select to show data points</span>
  <span class="p">)</span>
<span class="n">marker_cluster</span><span class="o">.</span><span class="n">add_to</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">locations</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_night_aftercovid_avg</span><span class="p">)):</span>
    <span class="n">row</span> <span class="o">=</span><span class="n">df_night_aftercovid_avg</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="n">location</span> <span class="o">=</span> <span class="p">[(</span><span class="n">row</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span><span class="n">row</span><span class="o">.</span><span class="n">lon</span><span class="p">)]</span><span class="o">*</span><span class="nb">int</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;post2019_hourly_counts&#39;</span><span class="p">])</span>
    <span class="n">locations</span> <span class="o">+=</span> <span class="n">location</span>
<span class="n">marker_cluster</span>  <span class="o">=</span> <span class="n">MarkerCluster</span><span class="p">(</span>
  <span class="n">name</span><span class="o">=</span><span class="s1">&#39;Night traffic after Covid-19&#39;</span><span class="p">,</span>
  <span class="n">locations</span><span class="o">=</span><span class="n">locations</span><span class="p">,</span>
  <span class="n">overlay</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
  <span class="n">control</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
  <span class="p">)</span> 
<span class="n">marker_cluster</span><span class="o">.</span><span class="n">add_to</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">folium</span><span class="o">.</span><span class="n">LayerControl</span><span class="p">()</span><span class="o">.</span><span class="n">add_to</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">m</span>
</code></pre></div>
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<h2 id="Clustering:-Night-Traffic">Clustering: Night Traffic<a class="anchor-link" href="#Clustering:-Night-Traffic">&#182;</a></h2><p>Discover if any patterns in night traffic, the night hours used were from 6pm to 4am.</p>

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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1">#identify class and features</span>
<span class="n">use_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;sensor_id&quot;</span><span class="p">,</span><span class="s2">&quot;lat&quot;</span><span class="p">,</span><span class="s2">&quot;lon&quot;</span><span class="p">,</span><span class="s2">&quot;hourly_counts&quot;</span><span class="p">]</span>
<span class="n">loc_night</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">df_night</span><span class="p">[</span><span class="n">use_list</span><span class="p">])</span>
<span class="c1">#print(loc_night.head(5))</span>

<span class="n">codes</span> <span class="o">=</span> <span class="n">loc_night</span><span class="p">[[</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">]]</span> <span class="c1">#class</span>
<span class="n">loc_night</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;sensor_id&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="n">loc_night</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
</code></pre></div>
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      <th></th>
      <th>lat</th>
      <th>lon</th>
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  </thead>
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    <tr>
      <th>1</th>
      <td>-37.81538</td>
      <td>144.974151</td>
      <td>240</td>
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      <th>2</th>
      <td>-37.81538</td>
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      <td>158</td>
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      <th>3</th>
      <td>-37.81538</td>
      <td>144.974151</td>
      <td>118</td>
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      <th>4</th>
      <td>-37.81538</td>
      <td>144.974151</td>
      <td>131</td>
    </tr>
    <tr>
      <th>5</th>
      <td>-37.81538</td>
      <td>144.974151</td>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1"># identify optimal clusters - these may be the same or different to day</span>
<span class="n">features_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;lat&quot;</span><span class="p">,</span><span class="s2">&quot;lon&quot;</span><span class="p">,</span><span class="s2">&quot;hourly_counts&quot;</span><span class="p">]</span>
<span class="n">df_features</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">loc_night</span><span class="p">[</span><span class="n">features_list</span><span class="p">])</span>

<span class="c1">#scale the features</span>
<span class="n">mms</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
<span class="n">mms</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df_features</span><span class="p">)</span> <span class="c1">#day hours</span>
<span class="n">data_transformed</span> <span class="o">=</span> <span class="n">mms</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_features</span><span class="p">)</span>

<span class="c1">#data_transformed</span>

<span class="c1"># for each k value calculate sum of squared distances to the nearest cluster centre</span>
<span class="n">Sum_of_squared_distances</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">K</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">)</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">K</span><span class="p">:</span>
    <span class="n">km</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">k</span><span class="p">)</span>
    <span class="n">km</span> <span class="o">=</span> <span class="n">km</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data_transformed</span><span class="p">)</span>
    <span class="n">Sum_of_squared_distances</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">km</span><span class="o">.</span><span class="n">inertia_</span><span class="p">)</span>
    
<span class="c1">#plot results</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">K</span><span class="p">,</span> <span class="n">Sum_of_squared_distances</span><span class="p">,</span> <span class="s1">&#39;g*-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Number of Clusters&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Sum of squared distances&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Elbow Method&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
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969mTp1KjExMTz00EMAnDhxgkaNGpk0XH1mobZgZKuRHMg6QOLFRHPHEUIIkzFqZoB+/frxwAMPoFar8fLyAirmERszZoxJw9V3g+4bxLwD81h+dDlhDcLMHUcIIUzC6OHNnp6e5ObmEh8fD1QUmtpcrbI+sre0Z2iLofzw2w+cvXrW3HGEEMIkjCo0v//+Oy+//DLLly/no48+AiA5Odnwvai+4cHDsVBZsOrYKnNHEUIIkzCq0KxcuZJBgwaxaNEiNJqKq20tW7bkxIkTJg13L/Cy96KvX182pm7kcvFlc8cRQogaZ1ShycjIoGvXrpXabGxsKCkpMUmoe83okNEUlBXwScon5o4ihBA1zqhC4+HhwenTpyu1paenGwYGiDsT7BZMF+8urD6+mpJyKd5CiPrFqEIzaNAg5syZw+eff05ZWRmbN29mwYIFPP3006bOd88YHTKaCwUX+N/p/5k7ihBC1CijCk27du2YNm0aV69epWXLlly6dInXXnuNNm3amDrfPaNH4x4EuQSx/MhyZIkgIUR9YvQKm76+vowcOdKUWe5pKpWKUa1H8dru1/jl/C90bdT19jsJIcRdwKgezbx580hJSanUlpKSwvz58406ybJlyxg5ciSvvvrqDT/75ptvGDhwIFevXgVAURRWr15NdHQ0r732WqV7Qzt27GDChAlMmDCBHTt2GNpPnz7Nq6++SnR0NKtXrzb0CPLz85k5cyYTJkxg5syZ5OfnG5XXXJ4IeAJ3W3dWHF1h7ihCCFFjjCo0ycnJ3HfffZXagoKCOH78uFEniYiIYNq0aTe0Z2dnc+TIEdzd3Q1thw4d4sKFC8TExDBq1ChWrap4viQ/P58vv/yS2bNnM3v2bL788ktD4Vi5ciWjR48mJiaGCxcukJSUBMCWLVto3bo1MTExtG7dmi1bthiV11xsNDYMazmMuD/iOJkrE5YKIeoHowqNpaUlRUVFldqKioqwsLAw6iQtW7bEwcHhhvZ169bx7LPPolKpDG2JiYl069YNlUpFUFAQ165dIzc3l6SkJEJCQnBwcMDBwYGQkBCSkpLIzc2lsLCQoKAgVCoV3bp1IyEhAYCEhAS6d+8OQPfu3Q3tddnzLZ/HxsJGejVCiHrDqHs0bdq0YcWKFYwaNQo7OzsKCgqIjY0lNDS02idOSEhAq9XSrFmzSu3XlyS4zs3NDZ1Oh06nw83NzdCu1Wpv2n59e4ArV67g6uoKgIuLC1euXLllnm3btrFt2zYA5syZUylDVWg0mmrvC+COO0NDhrL+yHrmPjSXBvYNqn2smsxlKpKraiRX1UiuqjNFNqMKzXPPPcfixYsZMWIEDg4O5OfnExoaSnR0dLVOWlxczObNm5k+fXq19q8OlUpVqef0d1FRUURFRRleZ2dnV+s87u7u1d73uiEBQ1h5aCULf1nIa+1eu6Nj1WQuU5BcVSO5qkZyVd2dZPP29r5pu1GFxsHBgalTp3L58mWys7Nxd3fHxcWlWkEALl68SFZWFq+//joAOTk5TJ48mffffx+tVlvpTebk5KDVatFqtSQnJxvadTodLVu2RKvVkpOTc8P2AM7OzuTm5uLq6kpubi5OTk7VzlybAlwCeLDJg6xLXse4NuOw1diaO5IQQlSb0bM3Q0WvwNHRkeLiYi5evMjFixerddImTZqwatUqli5dytKlS3Fzc2Pu3Lm4uLgQFhbGrl27UBSFkydPYmdnh6urK6GhoRw+fJj8/Hzy8/M5fPgwoaGhuLq6Ymtry8mTJ1EUhV27dhEWVjHlflhYGDt37gRg586dtG/fvlp5zWF0yGh0RTq+TPvS3FGEEOKOGNWjSUpK4qOPPuLy5cs3/GzTpk233X/RokUkJyeTl5fHmDFjGDhwIJGRkTfdtm3bthw8eJAJEyZgZWXF2LFjgYpe1ZNPPsnUqVMBGDBggGGAwciRI1m2bBklJSWEhobStm1boGIdnYULFxIXF4eHhweTJk0y5u3WCeFe4YS4h7Di6Aqebf4salWV/iYQQog6Q6UY8Rh6dHQ0jz32GBEREVhZWdVGLrM7f/58tfaryWuvW9K3MG77ONb0WkOvpr3u6Fh19Zqw5KoayVU1kqvqTHGPxqg/k/Pz83nwwQfvmSJTV/Tx64O3vbcMdRZC3NWMKjSRkZFs377d1FnE31iqLXmh1QvszdzLkUtHzB1HCCGqxah7NGlpaWzdupWvv/76htFm77zzjilyif/zTPNnWHhwISuOrmBJ5BJzxxFCiCozqtBERkbe8ua9MC0nKyeeaf4MscdimfrAVBo5NDJ3JCGEqBKjCk1ERISJY4h/8kLwC8Qei2X18dW82eFNc8cRQogqMXqZgMuXL5Oenk5eXl6l9VKkp2N6jR0b08e3D5+kfMLEthNxtHI0dyQhhDCaUYVm//79LF68mIYNG/LHH3/g4+PDH3/8QfPmzaXQ1JLRIaP53+n/8VnqZ4xqPcrccYQQwmhGjTrbtGkTY8eO5YMPPsDGxoYPPviAUaNG4evra+p84v+EeoTSwasDscdiKdOXmTuOEEIYzahCk52dTceOHSu1de/enV27dpkklLi50a1Hk5GfwXdnvjN3FCGEMJpRhcbJyckw/YyHhwcnT57k4sWL6PV6U2YTf/Ng0wfxdfJlxdEVGDGhgxBC1AlGFZqePXty4sQJAPr06cM777zD66+/Tq9edzYtiqgatUrNi61fJOlSEvsv7Dd3HCGEMIpRgwH69etn+L579+4EBwdTVFRE48aNTZVL3MLAoIH8O/HfrDi6gg4NO5g7jhBC3JZRPZoPPvig0mt3d3caN27MvHnzTBJK3JqtxpbnWj7Hj2d/5PSV0+aOI4QQt2VUoTl+/HiV2oVpDWs5DEu1JauOrTJ3FCGEuK1/vHR2fa2ZsrKyG9aduXjxIh4eHqZLJm7J086TJwKeYFPqJl5r9xpaG625IwkhxC39Y48mJyeHnJwc9Hq94fvrX+7u7rzyyiu1lVP8zajWoygqL+I/Kf8xdxQhhPhH/9ijub66ZVBQEFFRUdU+ybJlyzh48CDOzs7Mnz8fgP/85z8cOHAAjUZDgwYNGDt2LPb29gBs3ryZuLg41Go1w4cPJzQ0FKhY6XPNmjXo9Xp69uxpGKSQlZXFokWLyMvLw8/Pj+joaDQaDaWlpSxZsoTTp0/j6OjIxIkT8fT0rPb7qEuaa5sT0TiCtcfXMiZkDNYW1uaOJIQQN2XUPZrmzZsbnqMpKiri888/54svvqC4uNiok0RERDBt2rRKbSEhIcyfP5958+bRsGFDNm/eDEBGRgbx8fEsWLCAN954g9jYWPR6PXq9ntjYWKZNm8bChQvZs2cPGRkZAGzYsIE+ffqwePFi7O3tiYuLAyAuLg57e3sWL15Mnz59+OSTT4zKe7cY3Xo0WYVZbEnfYu4oQghxS0YVmg8//JCCggIA1q9fT0pKCmlpaaxYYdzKjy1btsTBwaFSW5s2bbCwsAAqekw6nQ6AhIQEOnXqhKWlJZ6ennh5eZGenk56ejpeXl40aNAAjUZDp06dSEhIQFEUjh8/Tnh4OFBR1BISEgBITEw0zDwdHh7OsWPH6tWDjl0bdaWFtoU8wCmEqNOMeo4mKysLb29vFEVh//79LFiwACsrK8aPH18jIeLi4ujUqRMAOp2OwMBAw8+0Wq2hCLm5uRna3dzcSEtLIy8vDzs7O0PR+uv2Op3OsI+FhQV2dnbk5eXh5OR0Q4Zt27axbds2AObMmYO7u3u13otGo6n2vtXxasdXGfndSA7lHaKX360foK3tXMaSXFUjuapGclWdKbIZVWisrKwoLCwkIyMDd3d3nJycKC8vp7S09I4DfPXVV1hYWNC1a9c7PtadiIqKqnQfKjs7u1rHcXd3r/a+1dGzQU8a2DVg3i/zuN/p/jqTy1iSq2okV9VIrqq7k2ze3t43bTfq0lnnzp159913Wbp0qeFS1JkzZ+74xvqOHTs4cOAAEyZMQKVSARU9kpycHMM2Op0OrVZ7Q3tOTg5arRZHR0cKCgooLy+vtP3fj1VeXk5BQQGOjvVrLRcrCyuGBw9n57mdpOhSzB1HCCFuYFShGTZsGE8//TQjR46kd+/eAKhUKp5//vlqnzgpKYmvv/6ayZMnY23954ipsLAw4uPjKS0tJSsri8zMTAICAvD39yczM5OsrCzKysqIj48nLCwMlUpFcHAw+/btAyqKV1hYGADt2rVjx44dAOzbt4/g4GBDQatPhjQfgq3GlhVHjbtnJoQQtUml1MJd5EWLFpGcnExeXh7Ozs4MHDiQzZs3U1ZWZhgkEBgYyKhRFQt6ffXVV2zfvh21Ws2wYcNo27YtAAcPHmTdunXo9Xp69OhB//79gYqHRxctWkR+fj6+vr5ER0djaWlJSUkJS5Ys4cyZMzg4ODBx4kQaNGhgVObz589X672aq0v8xp43+OTEJ/w6+Fca2N34HutqV11yVY3kqhrJVXWmuHR2y0Iza9Ys3njjDQBmzJhxy57AO++8U61Add3dVmjOXDlD18+7Mj50PFPaT6kzuW5HclWN5KoayVV1pig0txwM0L17d8P3slxz3efr7EvvZr35T8p/mBA6ATtLO3NHEkII4B8KTZcuXQzfXx8AIOq20a1Hs/W3rXx+8nOGBQ8zdxwhhAD+odBcf7r+dqS3U3eENQijrWdbVh5bydAWQ7FQW5g7khBC3LrQ7N692/C9oiikpqbi4uKCm5sbOTk5XL58mebNm0uhqUNUKhWjW49mzM9j+H+//z96N+tt7khCCHHrQvPWW28Zvl+9ejXt27enT58+hrbvv/+eCxcumDadqLKHmz2Mj4MPy48sl0IjhKgTjHqOZvfu3Tz88MOV2nr37l2p1yPqBo1aw8jWI9l/cT+Hsg6ZO44QQhhXaFxcXEhMTKzUlpiYeNM5w4T5PR30NE5WTiw/utzcUYQQwri5zoYPH878+fP53//+h5ubG9nZ2WRkZMjCZ3WUg5UDQ5oP4eOjH/NH3h/4OPqYO5IQ4h5mVI8mJCSEJUuW0KtXL3x9fenVqxdLliyhTZs2ps4nqml48HDUqFl1bJW5owgh7nFG9WgAHB0d6datmymziBrk7eBNX/++fJb6Ga/c/wru1M0pyYUQ9Z9RPRpxdxrdejTXSq/x6YlPzR1FCHEPk0JTj7Vyb0Wnhp1YcXQFkf+JJKsgy9yRhBD3ICk09dzokNFkFWYRnxHPwoMLzR1HCHEPMvoejbj7+K32o7i8GAAFhfUp61mfsh5rC2tOjzht5nRCiHvFLQvNSy+9ZNQBPvrooxoLI2rW3qf38u6+d/nuzHeU6kuxVFvyqO+jzAifYe5oQoh7yC0LTXR0tOH79PR0du7cycMPP4yHhweXLl3ixx9/lFFodVwDuwY4WjlSpi9DhYpSfSk2Ghs87e5sCW4hhKiKWxaali1bGr6PjY3ljTfeQKvVGtratm3L7Nmzeeyxx257kmXLlnHw4EGcnZ2ZP38+APn5+SxcuJBLly7h4eHBpEmTcHBwQFEU1qxZw6FDh7C2tmbs2LH4+fkBFcs0f/XVVwD079/fsHzB6dOnWbp0KSUlJbRt25bhw4ejUqlueY57SXZhNs+1eI4I/wiGfzucXy/8au5IQoh7jFGDAXQ6HTY2NpXabGxs0Ol0Rp0kIiKCadOmVWrbsmULrVu3JiYmhtatW7NlyxYADh06xIULF4iJiWHUqFGsWlXxwGF+fj5ffvkls2fPZvbs2Xz55Zfk5+cDsHLlSkaPHk1MTAwXLlwgKSnpH89xL1n14Cpmd5nNM62fYUDgADLyMjhz5Yy5Ywkh7iFGFZqwsDDmzp3LkSNHyMjI4PDhw8ybN4927doZdZKWLVve0JNISEgwrOLZvXt3EhISgIo51Lp164ZKpSIoKIhr166Rm5tLUlISISEhODg44ODgQEhICElJSeTm5lJYWEhQUBAqlYpu3boZjnWrc9yrpj0wDUsLS9799V1zRxFC3EOMGnX24osv8sUXX7By5Up0Oh1arZbw8HCeeuqpap/4ypUruLq6AhWTdl65cgWo6D25u//5FLubmxs6nQ6dToebm5uhXavV3rT9+vb/dI6b2bZtG9u2bQNgzpw5lTJUhUajqfa+pqTRaAhuEsy0LtN4Y/sbHLx6kF5+vcwdq05/XpLLeJKraupqLjBNNqMKjZWVFc8++yzPPvtsjZ78OpVKhUqlMsmxjT1HVFQUUVFRhtfZ2dnVOo+7u3u19zWl67kG+w5m5YGVTPpxEtue3Ial2rJO5KprJFfVSK6qqau54M6yeXt737Td6Ac2jxw5wkcffcScOXMAOHXqFMeOHatWGABnZ2dyc3MByM3NNSw5oNVqK73JnJwctFotWq2WnJwcQ/v1ntXf269v/0/nuJdZW1jzdvjbpF9OZ83xNeaOI4S4BxhVaLZu3crKlStp2LAhKSkpQEUvZ+PGjdU+cVhYGDt37gRg586dtG/f3tC+a9cuFEXh5MmT2NnZ4erqSmhoKIcPHyY/P5/8/HwOHz5MaGgorq6u2NracvLkSRRFYdeuXYSFhf3jOe51UU2iiPSJZMGBBVwquGTuOEKIes6oS2fff/89b775Jp6ennz99dcANGrUiPPnzxt1kkWLFpGcnExeXh5jxoxh4MCB9OvXj4ULFxIXF2cYegwVw6YPHjzIhAkTsLKyYuzYsQA4ODjw5JNPMnXqVAAGDBhgGGAwcuRIli1bRklJCaGhobRt2xbglue416lUKt4Kf4ueX/ZkbuJc5nWbZ+5IQoh6zKhCU1hYeMPNobKyMjQa42awmThx4k3bZ8y48Ql1lUrFyJEjb7p9ZGQkkZGRN7T7+/sbns/5K0dHx5ueQ0CASwAvtHqBFUdXMLTFUNp4yNpCQgjTMOrSWYsWLW54BmXr1q0EBwebIpOoJZPun4S7rTtvxr+JoijmjiOEqKeMKjQjRoxg//79jBs3jqKiIl5++WX27t3L888/b+p8woQcrRyZ2n4qB7IO8FX6V+aOI4Sop2577Uuv13Pu3Dneffddfv/9dy5duoSbmxsBAQGo1bLKwN3uqaCnWJ+yntn7Z9O7WW/sLe3NHUkIUc/ctlKo1Wo++OADrKysCAgIoGPHjgQFBUmRqSfUKjXvdnyXCwUXiEmKMXccIUQ9ZPQ9mpMnT5o6izCTdg3aMSBwACuOrJB50IQQNc6oYWMeHh68//77hIWF4ebmVukJ+0GDBpksnKg90x6YxtbftvLur++yppc8yCmEqDlG9WhKSkpo3749KpUKnU5HTk6O4UvUDw3sGvBy6Mv8dPYndvyxw9xxhBD1iFE9musPTYr6bWTrkXya+ilv7XuLbY3MPw+aEKJ+qNId/cLCQrKysrh48aLhS9QfMg+aEMIUjOrRZGRkEBMTw9mzZ2/42aZNm2o8lDCfqCZR9GjcgwUHFtA/oD/utnVzKnMhxN3DqB7NqlWrCA4OZvXq1djZ2bFmzRoefPBBxo0bZ+p8opapVCre7vg2hWWFzE2Ya+44Qoh6wKhCc/bsWZ599lns7e1RFAU7OzuGDBkivZl66vo8aJ+lfsaRS0fMHUcIcZczqtBYWlpSXl4OVExUmZ2djaIo5OfnmzScMJ+J90/EzdaNN/fKPGhCiDtjVKFp3rw5e/fuBSA8PJzZs2fz9ttvy6Sa9ZiTlRNT208l8WIim09tNnccIcRdzKjBAK+88orh+8GDB+Pj40NRURHdunUzWTBhfgODBrI+eT2zfp3FQ00fknnQhBDVUuUJy9RqNd26daNXr17Y2NiYIpOoI9QqNTM7zZR50IQQd8SoHs3ixYsrTTvzV+PHj7+jAN9++y1xcXGoVCp8fHwYO3Ysly9fZtGiReTl5eHn50d0dDQajYbS0lKWLFnC6dOncXR0ZOLEiXh6egKwefNm4uLiUKvVDB8+nNDQUACSkpJYs2YNer2enj170q9fvzvKe69p16AdTwY8yYojKxh832CaOTUzdyQhxF3GqB6Nl5cXDRo0MHxZW1tz6NAhw1LK1aXT6di6dStz5sxh/vz56PV64uPj2bBhA3369GHx4sXY29sTFxcHQFxcHPb29ixevJg+ffrwySefABXP+cTHx7NgwQLeeOMNYmNj0ev16PV6YmNjmTZtGgsXLmTPnj1kZGTcUeZ70bQHpqFRa3hn3zvmjiKEuAsZ1aN56qmnbmiLjIzkiy++uOMAer2ekpISLCwsKCkpwcXFhePHj/Pyyy8DEBERwRdffEGvXr1ITEw0ZAkPD2f16tUoikJCQgKdOnXC0tIST09PvLy8SE9PB/4skgCdOnUiISGBxo0b33Hue4mXvRcT205kdsJsdmbspHvj7uaOJIS4ixhVaG6mWbNmpKSk3NHJtVotjz32GC+99BJWVla0adMGPz8/7OzssLCwMGyj0+mAih6Qm5sbABYWFtjZ2ZGXl4dOpyMwMLDSca/vc33769+npaXdNMu2bdvYtm0bAHPmzMHdvXpPxGs0mmrva0p3mmtKxBQ2pW/i3f3vktg6EUuLmpkHrb5+XqYiuapGclWdKbIZVWiOHTtW6XVxcTF79uy5455Bfn4+CQkJLF26FDs7OxYsWEBSUtIdHbO6oqKiiIqKMrzOzs6u1nHc3d2rva8p1USu6e2nM/yn4czbNY8XW79YZ3KZguSqGslVNXU1F9xZNm9v75u2G1VoPvroo0qvbWxsaNq0qeHyVnUdPXoUT09PnJycAOjQoQOpqakUFBRQXl6OhYUFOp0OrVYLVPRUcnJycHNzo7y8nIKCAhwdHQ3t1/11n7+25+TkGNpF1T3Y5EF6NO7B/APzeSLgCZkHTQhhFKMKzdKlS01ycnd3d9LS0iguLsbKyoqjR4/i7+9PcHAw+/bto3PnzuzYsYOwsDAA2rVrx44dOwgKCmLfvn0EBwejUqkICwsjJiaGRx99lNzcXDIzMwkICEBRFDIzM8nKykKr1RIfH8+ECRNM8l7uBdfnQev5ZU/mJszl393+be5IQoi7gFGFRq/XG3Uwtbpqj+UEBgYSHh7O5MmTsbCwoFmzZkRFRXH//fezaNEiNm7ciK+vL5GRkUDFAIQlS5YQHR2Ng4MDEydOBMDHx4eOHTvyyiuvoFareeGFFwxZRowYwaxZs9Dr9fTo0QMfH58qZRSVBbgEMKLVCFYeXcnQFkMJ8QgxdyQhRB2nUoyYyMrY5Zrr0ySb58+fr9Z+dfXaa03mulpyla6fd6WZUzO2PLblls9Y1XaumiS5qkZyVU1dzQVmvEczYsQI9u3bxxNPPGEI8fXXX9OhQwfatm1brUDi7nV9HrRXd73K5lOb6R/Q39yRhBB1mFGF5ttvv2XOnDnY21fMdeXt7Y2fnx9Tp06lV69eJg0o6iaZB00IYSyjbqoUFBRQXFxcqa2kpISCggKThBJ1n1ql5t1O73Kh4AKLkxabO44Qog4zqkfTvXt3Zs6cSZ8+fXBzcyMnJ4etW7fSvbs8IX4vC2sQxpMBT7L8yHKevu9pmQdNCHFTRhWaIUOG4OXlRXx8PLm5ubi4uPDQQw9VesBR3JumPTCNrb9t5d1977K612pzxxFC1EFGFRq1Wk2vXr3kfoy4gZe9Fy+3fZn3E96XedCEEDdl1D2aX375xTDr8fnz53nrrbd45513OHfunEnDibvDi61fpJlTM97a+xal+lJzxxFC1DFGFZpNmzYZlgRYv349/v7+tGjRglWrVpk0nLg7WFtY81b4W6RdTmPt8bXmjiOEqGOMKjRXr17FxcWFkpISUlNTGTx4MAMGDOC3334zcTxxt3iwyYNENI5gwcEFZBfWzQfRhBDmYVShcXJy4sKFCyQlJeHv74+lpSWlpXKJRPxJpVLxTsd3KCgtYG7CXHPHEULUIUYVmieffJLJkyfz0Ucf0bdvX6Bi5uWmTZuaNJy4u1yfB+2z1M84cumIueMIIeoIo0adRURE0LFjRwCsra2Bigkxr09qKcR1k+6fxFfpXzFj7ww2P7b5juZBE0LUD0ZPt2xtbW0oMgDOzs64uLiYIpO4izlZOTElbAoJFxPYcmqLueMIIeqAqs3rL4QRBt03iDbubXjv1/e4VnrN3HGEEGYmhUbUOJkHTQjxV7csND/88IPh+wsXLtRKGFF/hDUIo39Af5YfWc5vV38zdxwhhBndcjDAZ599Ru/evQGYPHky69atM0mAa9eu8fHHH/PHH3+gUql46aWX8Pb2ZuHChVy6dAkPDw8mTZqEg4MDiqKwZs0aDh06hLW1NWPHjsXPzw+AHTt28NVXXwHQv39/IiIiADh9+jRLly6lpKSEtm3bMnz4cLlBXUveeOANfvjtB5kHTYh73C0LTYMGDVi/fj2NGzemrKyMuLi4m253fZnl6lqzZg2hoaG8+uqrlJWVUVxczObNm2ndujX9+vVjy5YtbNmyhSFDhnDo0CEuXLhATEwMaWlprFq1itmzZ5Ofn8+XX37JnDlzAJgyZQphYWE4ODiwcuVKRo8eTWBgIO+//z5JSUmyWFst+es8aLsydtGtcTdzRxJCmMEtL51NnDiRgoIC9uzZQ3l5Obt3777p150oKCggJSXFUKw0Gg329vYkJCQYliDo3r07CQkJACQmJtKtWzdUKhVBQUFcu3aN3NxckpKSCAkJwcHBAQcHB0JCQkhKSiI3N5fCwkKCgoJQqVR069bNcCxRO67PgzZj7wyZB02Ie9QtezTe3t6MGTMGgHfffZcZM2bU+MmzsrJwcnJi2bJlnD17Fj8/P4YNG8aVK1dwdXUFwMXFhStXrgCg0+lwd3c37O/m5oZOp0On0+Hm5mZo12q1N22/vv3NbNu2jW3btgEwZ86cSuepCo1GU+19Tcmcueb1mseALwfw5dkviW4fXWdy/RPJVTWSq2rqai4wTTajHticMWMG5eXlpKamGn55BwUFYWFhcUcnLy8v58yZM4wYMYLAwEDWrFnDli1bKm2jUqlq5Z5KVFRUpfV1srOrN1+Xu7t7tfc1JXPmCncJJ6JxBO/uepdeXr1ws/2z+MvnVTWSq2okV9XdSTZvb++bths1vPn8+fNMmjSJmJgYtm7dyocffsjEiRMNSwdUl5ubG25ubgQGBgIQHh7OmTNncHZ2Jjc3F4Dc3FycnJyAip7KXz+AnJwctFotWq2WnJwcQ7tOp7tp+/XtRe2qNA9aosyDJsS9xqhCs3LlSqKiovjoo4+YNWsWH3/8MQ8++CCxsbF3dHIXFxfc3Nw4f/48UDF/WuPGjQkLC2Pnzp0A7Ny5k/bt2wMQFhbGrl27UBSFkydPYmdnh6urK6GhoRw+fJj8/Hzy8/M5fPgwoaGhuLq6Ymtry8mTJ1EUhV27dhEWFnZHmUX1BLgEMDx4OJ+e+JSj2UfNHUcIUYuMunT222+/8eabb1a6hNWnTx82b958xwFGjBhBTEwMZWVleHp6MnbsWBRFYeHChcTFxRmGNwO0bduWgwcPMmHCBKysrBg7diwADg4OPPnkk0ydOhWAAQMGGNbPGTlyJMuWLaOkpITQ0FAZcWZGr7R7hc2nNvNm/JsyD5oQ9xCjCo1WqyU5OZlWrVoZ2lJSUgw37O9Es2bNDMOS/+pmgw9UKhUjR4686XEiIyNvOtTa39+f+fPn33FOceeuz4P22u7X2HJqC08EPGHuSEKIWmBUoRk8eDBz586lXbt2hhtFBw8eJDo6+vY7C/EXg+4bxPqU9by3/z16Ne2FO3Vz5I0QouYYdY8mLCyMuXPn4uPjQ1FRET4+PsyZM8dw70QIYxnmQbt2gTkJc+i5oSdZBVnmjiWEMCGjejRQMWztySefNGUWcY9o36A9/QP6s/b4WhQUFh5cyPtd3jd3LCGEiRhdaISoKX6r/SguLza8Xp+ynvUp67G2sOb0iNNmTCaEMAVZJkDUur1P76Wffz8s1ZaGtob2DZnbZS5l+jIzJhNCmIIUGlHrGtg1wNHKkTJ9GVYWVgDoinRM3DmRBz57gPf3v8/pK9KzEaK+kEIjzCK7MJvnWjzHnuf38HyL54loHEHsg7G0dm/NsiPL6Pp5V5785km+OPkFhWWF5o4rhLgDKkVRlNttlJ2dzRdffMFvv/1GUVFRpZ99+OGHJgtnTtdnK6iqujqH0d2U68K1C3yR9gUbUzfy29XfcLR05HH/xxncfDBt3NvUyoOed9PnVRdIrqqpq7nANHOdGTUYYMGCBXh7ezNw4ECsrKyqFUAIY3nZexEdGs34NuP59cKvfJb6GV+mfcmGExtooW3BoKBBPBn4JFobmbdOiLuBUYXm3LlzvPfee6jVcqVN1B6VSkV4w3DCG4Yzs9NMvj71NZtSN/H2vreZvX82vZr2YvB9g+naqCsW6jubSVwIYTpGFZp27drdMAWNELXJycqJoS2GMrTFUFJ0KWxM3ch/0/7Lt2e+xdvem4FBAxkUNIgmTk3MHVUI8TdG3aPJz89n+vTpNGjQAGdn50o/uz6xZX0j92hqx53kKi4v5qezP7ExdSM7M3aioNDFuwuD7xtM72a9sdHYmCWXKUmuqpFcVWe2ezTLli1DrVbTqFEjuUcj6gxrC2se83uMx/we41z+OT4/+TmbUjcxbvs4XKxdeML/CZ5u/jSt3KQnLoQ5GVVojh07xvLly7G1tTV1HiGqpZFDIybdP4mX277MnvN72Ji6kU9TP2VN8hpaubVi8H2D6RfQDxdrF3NHFeKeY9Td/aZNm5KXl2fqLELcMbVKTddGXVkauZQDzxzgvU7voaDwRvwbtPukHePjxvPLuV/QK3pzRxXinmFUjyY4OJhZs2YRERFxwz2am60BI0Rd4GrjyvDg4QwPHs6x7GN8lvoZm9M3s/nUZpo4NmFg0EAGBg2kkUMjc0cVol4zqtCkpqai1Wo5cuTIDT+riUKj1+uZMmUKWq2WKVOmkJWVxaJFi8jLy8PPz4/o6Gg0Gg2lpaUsWbKE06dP4+joyMSJE/H09ARg8+bNxMXFoVarGT58OKGhoQAkJSWxZs0a9Ho9PXv2pF+/fnecV9x9Wrm3Ypb7LKZ3mM4Pv/3AxtSNzDswj/kH5hPROIKn73uaXk17YWVhxcWCiwzaMIjF3Rbjaedp7uhC3PWMKjRvvfWWSUN8//33NGrUiMLCiqlGNmzYQJ8+fejcuTMrVqwgLi6OXr16ERcXh729PYsXL2bPnj188sknTJo0iYyMDOLj41mwYAG5ubnMnDnTMGNBbGws06dPx83NjalTpxIWFkbjxo1N+n5E3WWrseWJgCd4IuAJfr/6O5tObmLTyU2M/nk0Whst/QP6c/HaReIz4mX5AiFqiFH3aPR6/S2/7lROTg4HDx6kZ8+eACiKwvHjxwkPDwcgIiKChIQEABITE4mIiAAgPDycY8eOoSgKCQkJdOrUCUtLSzw9PfHy8iI9PZ309HS8vLxo0KABGo2GTp06GY4lRBOnJrwe9jq/Pv0rG3pv4HLxZVYdW8U3Z75Br+hZn7KeRisb4bfaz9xRhbirGb2U861s2rTpjgKsXbuWIUOGGHozeXl52NnZYWFR8aS3VqtFp9MBoNPpcHNzA8DCwgI7Ozvy8vLQ6XQEBgYajvnXfa5vf/37tLS0O8or6h8LtQU9fHqQ+Ewi0/dM56fff6q0XEFJeQmPbH7EMEtBB68OOFs7/8MRhRB/ZVShWbJkSaXXubm5bNmyhbCwsDs6+YEDB3B2dsbPz4/jx4/f0bHu1LZt29i2bRsAc+bMwd29emvZazSaau9rSpLr9txxp1FyI/Rn9dhobCgpK+HhgIcJbRDK7j92szZ5LcuPLkeFipAGIXT16Uq3Jt3o4tMFNzu325+gBtSlz+uvJFfV1NVcYJpsRhUaDw+PG16PHz+eqVOn3tFggNTUVBITEzl06BAlJSUUFhaydu1aCgoKKC8vx8LCAp1Oh1ZbMXmiVqslJycHNzc3ysvLKSgowNHR0dB+3V/3+Wt7Tk6Oof3voqKiiIqKMryu7pOxdfWJX8llnIzcDIY2H8r4juNZsncJWYVZjG05lrEtx1JUVkTSpST2Zu5lb+ZeVh1axZLEij/Cmrs2p2PDjoZej7utaX6J1LXP6zrJVTV1NReYcWaAmykoKODq1avV3R2AZ555hmeeeQaA48eP88033zBhwgQWLFjAvn376Ny5Mzt27DD0nNq1a8eOHTsICgpi3759BAcHo1KpCAsLIyYmhkcffZTc3FwyMzMJCAhAURQyMzPJyspCq9USHx/PhAkT7iizqN9WPbgKqPifbXaX2ZV+ZqOxMRSSSUyipLyEw5cOszdzL/sy97Hp5CbWJK8BINAlkPCG4Ybi08CuQa2/FyHqCqMKzeLFiyutAVJcXExKSgpdu3Y1Sahnn32WRYsWsXHjRnx9fQ29psjISJYsWUJ0dDQODg5MnDgRAB8fHzp27Mgrr7yCWq3mhRdeMMw0PWLECGbNmoVer6dHjx74+PiYJLO491hZWNHeqz3tvdozoe0ESvWlHM0+yr7MfezN3Mvm9M38J+U/APg6+dKxYUc6enck3Cscb4eb/+UnRH1k1KSaX3zxRaXX1tbWNGvWjJCQEJMFMzeZVLN21OdcZfoyjuccN/R49l/Yz5WSKwA0dWxq6B11bNgRH0fj/gCqz5+XKUiuqjPbpbOnnnqqWicV4l6mUWto49GGNh5tGBMyhnJ9OSm5KezL3Me+zH38dPYnNp2sGLXZyKER4V7hhl5PU8emN6wkKg+SirvVPxaa06dPo9FoaNKkYo2Pq1evsnbtWv744w8CAwN57rnnsLGp/lTsQtxLLNQWtHJrRSu3VoxsNRK9oic1N9VwqW1Hxg7+m/5foGKV0Y5efw4u8Hf2Z9HBRfIgqbgr/WOhWbt2LQMGDDAUmo8//pjc3Fx69uzJnj172LBhAyNHjqyVoELUN2qVmhbaFrTQtmB48HAURSH9cnrFpbYL+9hzfg+bT22+Yb/1KetZn7IeawtrTo84bYbkQlTNPxaac+fO0aJFCwCuXbvGoUOHmD9/Pt7e3oSFhfHmm29KoRGihqhUKgJdAwl0DeS5ls+hKAqnr5zmp99/Yu3xtWTkZxi2tbGwoUfjHnx24jM6e3eWlUVFnfaPhaa8vByNpmKTtLQ0XFxcDDd73N3duXbtmukTCnGPUqlU+Lv485LLS5y9epYNKRuwsrCipLwEL3svErMS2Xp2KwCNHRrT2bsznb0708m7Ew3tG5o5vRB/+sdC4+Pjw969e+nUqRN79uyhdevWhp/pdDrs7OxMHlAIAdmF2TzX4rlKD5KujFpJ+uV09pzfw57ze/jx7I+GwQV+zn4VRadhJzp5dzLZA6RCGOMfhzefOHGCuXPnAqBWq5k5c6ahR/Ptt9+SlpbGpEmTaidpLZPhzbVDclXNP+XSK3qSdcnsObeH+Mx49mXuI780H4AW2hZ0atiJzt6dCW8YXuNztd2Nn5c51dVcYJrhzbd9jqawsJDMzEwaNmxYaSnn8+fPY2Njc8spXe52Umhqh+SqmqrkKtOXcST7CPHn49lzfg/7L+ynqLwItUpNK7dWhkttD3g9gL2lfa3lqk2Sq+rM8hyNra0tfn43TpN+qwMKIeoGjVrD/Z73c7/n/YwPHU9xeTFJWUmGS22xx2L56MhHaFQaQj1DDZfa2jVoh63G9vYnEMJI1Z7rTAhxd7G2sKZDww50aNiBV9q9QmFZIQkXE9hzfg/x5+NZkrSEDw99iLWFNe0829HJuxNdvLvQxqMNVhZW5o4v7mJSaIS4R9lqbOnWqBvdGnUDIK8kj18v/Gq41Db/wHzmHZiHncaOB7weMFxqa+XWCgu1heE4MmOBuB0pNEIIABytHIlqEkVUk4rlMnKLctmXuc9wqW3W/lkAOFk5Ed4w3HCpbX3yepmxQPwjKTRCiJtytXHlYd+Hedj3YQCyCrLYm7nXUHh+OvtTpe2vz1hgpbYi+flkuc8jDKTQCCGM4mnnyeP+j/O4/+MAHL50mOnx0zl86TDlSrlhuxJ9CYFrAvFx9CHAJYAAlwACXQIJdAnE38UfrU39HKkqbk0KjRCiWtp4tCHYLZhDWYew0dhQXFbMo76P8ojvI6RfTiftchrpl9OJPx9PUXmRYT83GzcCXQIrFyHXQLztvW+YsVrUD1JohBDVdrMZC/r69620Tbm+nIz8jErFJ+1yGt+e+ZbLxZcN29lp7PB38TcUoev/bObUTEa93eWMWvjsXiQPbNYOyVU19SmXoijkFOVUKj7puemkX0nnXP45w3YWKguaOTWrVHwCXQPxd/bH0crxlse/WHCRCbsm1MnRcHX13yOYceEzU8nOzmbp0qVcvnwZlUpFVFQUjzzyCPn5+SxcuJBLly7h4eHBpEmTcHBwQFEU1qxZw6FDh7C2tmbs2LGGh0l37NjBV199BUD//v2JiIgAKtbUWbp0KSUlJbRt25bhw4dL91yIOkClUuFu6467rTsdG3as9LNrpdc4dfmUoQhdL0Q///4zZUqZYTsve6+K4uMcQIDrn/eCPGw9ZP2eOsSshcbCwoKhQ4fi5+dHYWEhU6ZMISQkhB07dtC6dWv69evHli1b2LJlC0OGDOHQoUNcuHCBmJgY0tLSWLVqFbNnzyY/P58vv/ySOXPmADBlyhTCwsJwcHBg5cqVjB49msDAQN5//32SkpJo27atOd+2EOI27C3tCfEIIcSj8nLxpfpSzl49W+kyXPrldD5P+5xrpTefTf6vo+FODj+JpdqyNt6C+AuzFhpXV1dcXV2BiqluGjVqhE6nIyEhgbfffhuA7t278/bbbzNkyBASExPp1q0bKpWKoKAgrl27Rm5uLsePHyckJAQHBwcAQkJCSEpKIjg4mMLCQoKCggDo1q0bCQkJUmiEuEtZqi0Ngwh609vQrigKmdcySb+SzqGLh/gi7QvOXj2LHr1hmxJ9CQGrA2jq1BR/F38CnAPwd/Gv+HKW0XCmVGcGA2RlZXHmzBkCAgK4cuWKoQC5uLhw5coVoGJpAnf3P6c7d3NzQ6fTodPpcHNzM7Rrtdqbtl/f/ma2bdvGtm3bAJgzZ06l81SFRqOp9r6mJLmqRnJVTV3I5eHhQQgh9Kc/ufpcYpNisbGwoaSshL739aVvYF9SdamczDlJak4qOzJ2UFJeYtjf3dadILcg7nO7j0BtIPe53UeQNgg/Vz806pr9VVkXPq9bMUW2OlFoioqKmD9/PsOGDbthjRuVSlUr91SioqKIiooyvK7uzbC6epNPclWN5KqaupYrIzeDoc2HVhoN91DDh3io4UOGbcr15fyR/wfpl9M5dfkUp66c4tTlU3xz8huyC/98L5ZqS5o6Na3UA7r+vYu1S7Xy1bXP66/q3WAAgLKyMubPn0/Xrl3p0KEDAM7OzuTm5uLq6kpubi5OTk5ARU/lrx9ATk4OWq0WrVZLcnKyoV2n09GyZUu0Wi05OTk3bC+EqN9WPbgKqPilObvL7JtuY6GuGM3WzKmZYdqd6y4XX65UfE5dOUX65XR+/uNnSvWlhu3cbd3xd/YnwCUAP2c/AlwC8Hf2x8fR55a9oHtxbjizFhpFUfj4449p1KgRjz76qKE9LCyMnTt30q9fP3bu3En79u0N7T/88AOdO3cmLS0NOzs7XF1dCQ0N5bPPPiM/v2KRp8OHD/PMM8/g4OCAra0tJ0+eJDAwkF27dtG7d++bZhFCiOtcrF1o16Ad7Rq0q9Repi/j97zfKxWfU5dPsfW3reiK/rwsb6W2MgzJ9nPx+7M35Ox/T46GM+tzNCdOnGDGjBk0adLEcHls8ODBBAYGsnDhQrKzs28Y3hwbG8vhw4exsrJi7Nix+Pv7AxAXF8fmzZuBiuHNPXr0AODUqVMsW7aMkpISQkNDGTFihFGX4uQ5mtohuapGclVNbebSFek4deUUpy+frihA/1eIzl49W2lI9t9ZqCx4sfWL2Gvssbe0x8HKAQdLh4rv/++f1793sHTAVmNrstsJd/rsUbVX2LxXSaGpHZKraiRX1dSFXNeHZB/MOsiqo6s4kXuCcqUcFSrsNHZYa6wpKC2oNE3PP1GhqlSEDP+0cjC6WP319V8L19RfprLhxAaGNB9Srd5Wnb1HI4QQ9dlfh2QfzDpIsi7ZMDfck4FPGn6hl+nLuFZ6zfCVX5pPfmm+4fsb2ksqt2fkZVTa1tjCpVap0Sv6Sm3Xnz2ytrDm9IjTd/wZSKERQohacrO54a7TqDU4WzvjbO1cI+e6Xrj+WqyuF6hrZdcqFatLBZfYk7mHjPwM9IoeWwtbejfrzYzwGTWSRQqNEELUEmNGw9WUqhauKb9MYUPKBmw0NhSVFeFo5Vhjo+LUNXIUIYQQd7Xrva3dz+3muRbPcanwUo0dW3o0QgghTNrbkh6NEEIIk5JCI4QQwqSk0AghhDApKTRCCCFMSgqNEEIIk5JCI4QQwqRkrjMhhBAmJT2aGjZlyhRzR7gpyVU1kqtqJFfV1NVcYJpsUmiEEEKYlBQaIYQQJiWFpoZFRUXdfiMzkFxVI7mqRnJVTV3NBabJJoMBhBBCmJT0aIQQQpiUFBohhBAmJcsE1JBly5Zx8OBBnJ2dmT9/vrnjGGRnZ7N06VIuX76MSqUiKiqKRx55xNyxKCkp4a233qKsrIzy8nLCw8MZOHCguWMZ6PV6pkyZglarrTNDUceNG4eNjQ1qtRoLCwvmzJlj7kgAXLt2jY8//pg//vgDlUrFSy+9RFBQkFkznT9/noULFxpeZ2VlMXDgQPr06WPGVBW+/fZb4uLiUKlU+Pj4MHbsWKysrMwdi++//56ff/4ZRVHo2bNnzX5WiqgRx48fV06dOqW88sor5o5SiU6nU06dOqUoiqIUFBQoEyZMUP744w8zp1IUvV6vFBYWKoqiKKWlpcrUqVOV1NRUM6f60zfffKMsWrRIef/9980dxWDs2LHKlStXzB3jBosXL1a2bdumKErFv8v8/HwzJ6qsvLxcGTlypJKVlWXuKEpOTo4yduxYpbi4WFEURZk/f76yfft284ZSFOXs2bPKK6+8ohQVFSllZWXKu+++q2RmZtbY8eXSWQ1p2bIlDg4O5o5xA1dXV/z8/ACwtbWlUaNG6HQ6M6cClUqFjY0NAOXl5ZSXl6NSqcycqkJOTg4HDx6kZ8+e5o5S5xUUFJCSkkJkZCQAGo0Ge3t7M6eq7OjRo3h5eeHh4WHuKEBFb7mkpITy8nJKSkpwdXU1dyTOnTtHQEAA1tbWWFhY0KJFC3799dcaO75cOruHZGVlcebMGQICAswdBaj4H27y5MlcuHCBhx56iMDAQHNHAmDt2rUMGTKEwsJCc0e5waxZswB48MEH68QQ2aysLJycnFi2bBlnz57Fz8+PYcOGGf6IqAv27NlD586dzR0DAK1Wy2OPPcZLL72ElZUVbdq0oU2bNuaOhY+PDxs3biQvLw8rKysOHTqEv79/jR1fejT3iKKiIubPn8+wYcOws7MzdxwA1Go1//73v/n44485deoUv//+u7kjceDAAZydnQ29wLpk5syZzJ07l2nTpvHjjz+SnJxs7kiUl5dz5swZevXqxQcffIC1tTVbtmwxdyyDsrIyDhw4QHh4uLmjAJCfn09CQgJLly5l+fLlFBUVsWvXLnPHonHjxjz++OO89957zJ49m2bNmqFW11x5kB7NPaCsrIz58+fTtWtXOnToYO44N7C3tyc4OJikpCSaNGli1iypqakkJiZy6NAhSkpKKCwsJCYmhgkTJpg1F1T8NQzg7OxM+/btSU9Pp2XLlmbN5Obmhpubm6E3Gh4eXqcKzaFDh/D19cXFxcXcUYCKy3ienp44OTkB0KFDB06ePEm3bt3MnAwiIyMNl0A//fRT3NzcauzY0qOp5xRF4eOPP6ZRo0Y8+uij5o5jcPXqVa5duwZUjEA7cuQIjRo1MnMqeOaZZ/j4449ZunQpEydOpFWrVnWiyBQVFRku5RUVFXHkyBGzF2UAFxcX3NzcOH/+PFDxi7Rx48ZmTvWnunTZDMDd3Z20tDSKi4tRFIWjR4/Wif/uAa5cuQJUjFTdv38/Xbp0q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<h3 id="For-night-traffic-we-will-try-using-3-clusters">For night traffic we will try using 3 clusters<a class="anchor-link" href="#For-night-traffic-we-will-try-using-3-clusters">&#182;</a></h3>
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<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="c1"># run k-means clustering, define centroids</span>
<span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">loc_night</span><span class="p">)</span>
<span class="n">centroids</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">cluster_centers_</span>

<span class="n">codes</span><span class="p">[</span><span class="s1">&#39;cluster&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span>
<span class="c1">#codes.head()</span>
</code></pre></div>
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     <div class=" highlight hl-ipython3"><pre><code class="language-python" data-lang="python"><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">df_features</span>

<span class="c1">#visualise data</span>
<span class="n">x_axis</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;lon&#39;</span><span class="p">]</span>
<span class="n">y_axis</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">]</span>
<span class="n">cluster_legend</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">scatterplot</span><span class="p">(</span><span class="n">x_axis</span><span class="p">,</span> <span class="n">y_axis</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="n">cluster_legend</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">cluster_legend</span><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s1">&#39;colorblind&#39;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">60</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Clustering Pedestrian Counts: Night Hours&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
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<div class="usecase-section-header">Congratulations!</div>

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<p>You've successfully used Melbourne Open Data to visualise day and night pedestrian traffic in and around the City of Melbourne!</p>
<p>For next steps please explore the City of Melbourne Open Data playground, such as stepping through other the use cases on pedestrain traffic.</p>
<p>Try the usecase on finding a <a href="https://master-mop-busaytgm.ts.gateway.dev/use-cases/pedestrian-traffic-analysis">new business location with pedestrian foot traffic and survey data</a> or the use case on <a href="https://master-mop-busaytgm.ts.gateway.dev/use-cases/pedestrian-traffic-analysis">evaluating pedestrian traffic in one location</a> over a day, week and month when starting a new business venture.</p>

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<p>City of Melbourne Open Data Team, 2014 - 2021,'Pedestrian Counting System - Monthly (counts per hour)', City of Melbourne, date retrieved 11 Aug 2022, <a href="https://dev.socrata.com/foundry/data.melbourne.vic.gov.au/b2ak-trbp">https://dev.socrata.com/foundry/data.melbourne.vic.gov.au/b2ak-trbp</a></p>
<p>City of Melbourne Open Data Team, 2014 - 2021,'Pedestrian Counting System - Sensor Locations', City of Melbourne, date retrieved 26 Aug 2022, <a href="https://data.melbourne.vic.gov.au/Transport/Pedestrian-Counting-System-Sensor-Locations/h57g-5234">https://data.melbourne.vic.gov.au/Transport/Pedestrian-Counting-System-Sensor-Locations/h57g-5234</a></p>
<p>Carlini L 2019, 'Clustering and Visualisation using Folium Maps', Kaggle, retrieved 23 Sep 2022 <a href="https://www.kaggle.com/code/lucaspcarlini/clustering-and-visualisation-using-folium-maps/notebook#Folium-Maps-Visualisation-by-Number-of-Occurences-and-Clustering">https://www.kaggle.com/code/lucaspcarlini/clustering-and-visualisation-using-folium-maps/notebook#Folium-Maps-Visualisation-by-Number-of-Occurences-and-Clustering</a></p>

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     <div class=" highlight hl-ipython3"><span></span><span class="c1">#save notebook, required so that step to convert to html, writes latest results to file</span>
<span class="c1"># may need to adapt for other OS, this is for Windows</span>
<span class="n">keyboard</span><span class="o">.</span><span class="n">press_and_release</span><span class="p">(</span><span class="s1">&#39;ctrl+s&#39;</span><span class="p">)</span>

<span class="o">!</span>jupyter nbconvert  usecase_evaluate_business_location_using_pedestrian_traffic_day_night.ipynb --to html
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<pre><code class="language-python" data-lang="python">[NbConvertApp] Converting notebook usecase_evaluate_business_location_using_pedestrian_traffic_day_night.ipynb to html
[NbConvertApp] Writing 25826605 bytes to usecase_evaluate_business_location_using_pedestrian_traffic_day_night.html
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